23.2 Land Cover, General Problems, Remote Sensing

Chapter Contents (Back)
Classification. Remote Sensing. Land Cover. Ground Cover. The overlapping subset:
See also Land Use, General Problems.
See also Object Based Land Cover, Parcels, Region Based Land Cover, Land Use Analysis.
See also LAI, Leaf Area Index, Land Cover Analysis.
See also Land Cover, Land Use, Very High Resolution, High Spatial Resolution.
See also Subpixel Target, Subpixel Land Use, Tiny Objects.
See also Surface Fractional Vegetation Cover.
See also Classification for Urban Area Land Cover, Remote Sensing.
See also Land Cover Analysis, Specific Location Applications, Site Analysis, Site Specific.
See also Sentinel-1, -2, -3 for Land Cover, Remote Sensing.
See also Rice Crop Analysis, Production, Detection, Health, Change. For global scale analysis:
See also Global-Scale Analysis, Global Land Cover Analysis.
See also Plant Phenotyping.
See also Gross Primary Production, Net Primary Production, GPP, NPP.

Ince, F.[Fuat],
The application of the coalescence clustering algorithm to remotely sensed multispectral data,
PR(14), No. 1-6, 1981, pp. 121-126.
Elsevier DOI 0309

Sawada, N.[Nobuo], Numagami, H.[Hideo], Shinoda, H.[Hidenori], Kidode, M.[Masatsugu], Watanabe, S.[Sadakazu],
Application of a parallel pattern processor to remote sensing,
PR(14), No. 1-6, 1981, pp. 331-343.
Elsevier DOI 0309

Lobo, A.,
Image Segmentation and Discriminant-Analysis for the Identification of Land-Cover Units in Ecology,
GeoRS(35), No. 5, September 1997, pp. 1136-1145.
IEEE Top Reference. 9710

Kavzoglu, T., Mather, P.M.,
Pruning artificial neural networks: an example using land cover classification of multi-sensor images,
JRS(20), No. 14, September 1999, pp. 2787. BibRef 9909

Kavzoglu, T., Mather, P.M.,
The role of feature selection in artificial neural network applications,
JRS(23), No. 15, August 2002, pp. 2919-2937. 0211

Steele, B.M.[Brian M.],
Combining Multiple Classifiers. An Application Using Spatial and Remotely Sensed Information for Land Cover Type Mapping,
RSE(74), No. 3, 2000, pp. 545- 556. 0102

Liu, X.H.[Xue-Hua], Skidmore, A.K., van Oosten, H.,
Integration of classification methods for improvement of land-cover map accuracy,
PandRS(56), No. 4, July 2002, pp. 257-268.
HTML Version. 0207

Debeir, O.[Olivier], van den Steen, I.[Isabelle], Latinne, P.[Patrice], van Ham, P.[Philippe], Wolff, E.[Eléonore],
Textural and Contextual Land-Cover Classification Using Single and Multiple Classifier Systems,
PhEngRS(68), No. 6, June 2002, pp. 597.
WWW Link. 0207
Improve the accuracy of land-cover clasification with textural, contextual, and multiple classifier system. BibRef

Hlavka, C.A., Dungan, J.L.,
Areal Estimates of Fragmented Land Cover: Effects of Pixel Size and Model-Based Corrections,
JRS(23), No. 4, February 2002, pp. 711-724. 0202

King, R.B.,
Land cover mapping principles: a return to interpretation fundamentals,
JRS(23), No. 18, September 2002, pp. 3525-3545.
WWW Link. 0211

Huang, C., Davis, L.S., Townshend, J.R.G.,
An assessment of support vector machines for land cover classification,
JRS(23), No. 4, February 2002, pp. 725-749. 0202

Shao, G.F.[Guo-Fan], We, W.C.[Wen-Chun], Wu, G.[Gang], Zhou, X.H.[Xin-Hua], Wu, J.G.[Jian-Guo],
An Explicit Index for Assessing the Accuracy of Cover-Class Areas,
PhEngRS(69), No. 8, August 2003, pp. 907-914.
WWW Link. 0401
The accuracy of cover class areas is not strongly related to conventional classification accuracy assessment indices, but can be assessed with a new index called Relative Errors of Area (REA). BibRef

Kempeneers, P., de Backer, S., Debruyn, W., Coppin, P., Scheunders, P.,
Generic Wavelet-Based Hyperspectral Classification Applied to Vegetation Stress Detection,
GeoRS(43), No. 3, March 2005, pp. 610-614.
IEEE Abstract. 0501

Tran, L.T.[Liem T.], Wickham, J.D.[James D.], Jarnagin, S.T.[S. Taylor], Knight, C.G.[C. Gregory],
Mapping Spatial Thematic Accuracy with Fuzzy Sets,
PhEngRS(71), No. 1, January 2005, pp. 29-36.
WWW Link. 0509

Li, X.Z.[Xiu-Zhen], He, H.S.[Hong S.], Bu, R.[Rencang], Wen, Q.C.[Qing-Chun], Chang, Y.[Yu], Hu, Y.M.[Yuan-Man], Li, Y.H.[Yue-Hui],
The adequacy of different landscape metrics for various landscape patterns,
PR(38), No. 12, December 2005, pp. 2626-2638.
Elsevier DOI 0510

Chen, L.[Li],
Nested Hyper-Rectangle Learning Model for Remote Sensing: Land Cover Classification,
PhEngRS(71), No. 3, March 2005, pp. 333. The NHLM learning model is presented and tested with SPOT data to illustrate an efficient and accurate supervised classification method.
WWW Link. 0509

Herold, M., Woodcock, C., di Gregorio, A., Mayaux, P., Belward, A.S., Latham, J., Schmullius, C.C.,
A Joint Initiative for Harmonization and Validation of Land Cover Datasets,
GeoRS(44), No. 7, Part 1, July 2006, pp. 1719-1727.

Aitkenhead, M.J., Dyer, R.,
Improving Land-cover Classification Using Recognition Threshold Neural Networks,
PhEngRS(73), No. 4, April 2007, pp. 413-421.
WWW Link. 0704
Improving land-cover classification from remote sensing imagery with neural networks using a threshold of recognition below which the recognition system applies additional bootstrapped information to classify pixels. BibRef

Bagan, H.[Hasi], Wang, Q.X.[Qin-Xue], Watanabe, M.[Masataka], Kameyama, S.[Satoshi], Bao, Y.H.[Yu-Hai],
Land-cover Classification Using ASTER Multi-band Combinations Based on Wavelet Fusion and SOM Neural Network,
PhEngRS(74), No. 3, March 2008, pp. 333-342.
WWW Link. 0803
A land-cover classification methodology using ASTER VNIR, SWIR, and TIR band combinations based on wavelet fusion and SOM neural network methods, and classification accuracy of different band combinations. BibRef

Trias-Sanz, R.[Roger], Stamon, G.[Georges], Louchet, J.[Jean],
Using colour, texture, and hierarchial segmentation for high-resolution remote sensing,
PandRS(63), No. 2, March 2008, pp. 156-168.
Elsevier DOI 0803
Segmentation; Hierarchical; Colour; Cartography; Land cover BibRef

Tseng, M.H.[Ming-Hseng], Chen, S.J.[Sheng-Jhe], Hwang, G.H.[Gwo-Haur], Shen, M.Y.[Ming-Yu],
A genetic algorithm rule-based approach for land-cover classification,
PandRS(63), No. 2, March 2008, pp. 202-212.
Elsevier DOI 0803
Classification; Land-cover; Rule-based; Genetic algorithm; Knowledge rules BibRef

Mitrakis, N.E., Topaloglou, C.A., Alexandridis, T.K., Theocharis, J.B., Zalidis, G.C.,
Decision Fusion of GA Self-Organizing Neuro-Fuzzy Multilayered Classifiers for Land Cover Classification Using Textural and Spectral Features,
GeoRS(46), No. 7, July 2008, pp. 2137-2152.

Stavrakoudis, D.G., Theocharis, J.B., Zalidis, G.C.,
A Boosted Genetic Fuzzy Classifier for land cover classification of remote sensing imagery,
PandRS(66), No. 4, July 2011, pp. 529-544.
Elsevier DOI 1107
AdaBoost; Genetic fuzzy rule-based classification systems (GFRBCS); Local feature selection; Textural and spatial features; Multispectral image classification BibRef

Stavrakoudis, D.G., Galidaki, G.N., Gitas, I.Z., Theocharis, J.B.,
A Genetic Fuzzy-Rule-Based Classifier for Land Cover Classification From Hyperspectral Imagery,
GeoRS(50), No. 1, January 2012, pp. 130-148.

Mylonas, S.K.[Stelios K.], Stavrakoudis, D.G.[Dimitris G.], Theocharis, J.B.[John B.], Mastorocostas, P.A.[Paris A.],
A Region-Based GeneSIS Segmentation Algorithm for the Classification of Remotely Sensed Images,
RS(7), No. 3, 2015, pp. 2474-2508.
DOI Link 1504

Mylonas, S.K.[Stelios K.], Stavrakoudis, D.G.[Dimitris G.], Theocharis, J.B.[John B.], Mastorocostas, P.A.[Paris A.],
Classification of Remotely Sensed Images Using the GeneSIS Fuzzy Segmentation Algorithm,
GeoRS(53), No. 10, October 2015, pp. 5352-5376.
feature extraction BibRef

Smikrud, K.M.[Kathy M.], Prakash, A.[Anupma], Nichols, J.V.[Jeff V.],
Decision-based Fusion for Improved Fluvial Landscape Classification Using Digital Aerial Photographs and Forward Looking Infrared Images,
PhEngRS(74), No. 7, July 2008, pp. 903-912.
WWW Link. 0804
Comparing different image processing routines to classify macro fish habitat indicators in a large river floodplain using digital aerial photographs and forward looking infrared images leading to a decision-based fusion strategy to provide the best results. BibRef

Duca, R., del Frate, F.,
Hyperspectral and Multiangle CHRIS-PROBA Images for the Generation of Land Cover Maps,
GeoRS(46), No. 10, October 2008, pp. 2857-2866.

Li, Z.[Zhe],
Fuzzy ARTMAP-based Neurocomputational Spatial Uncertainty Measures,
PhEngRS(74), No. 12, December 2008, pp. 1573-1584.
WWW Link. 0804
Non-parametric Commitment and Typicality measures for the fuzzy ARTMAP computational neural network to handle spatial uncertainty in remotely sensed imagery classification. BibRef

Liu, X., Li, X., Liu, L., He, J., Ai, B.,
An Innovative Method to Classify Remote-Sensing Images Using Ant Colony Optimization,
GeoRS(46), No. 12, December 2008, pp. 4198-4208.

Lehner, P.E., Adelman, L., DiStasio, R.J., Erie, M.C., Mittel, J.S., Olson, S.L.,
Confirmation Bias in the Analysis of Remote Sensing Data,
SMC-A(39), No. 1, January 2009, pp. 218-226.

Wuest, B.[Ben], Zhang, Y.[Yun],
Region based segmentation of QuickBird multispectral imagery through band ratios and fuzzy comparison,
PandRS(64), No. 1, January 2009, pp. 55-64.
Elsevier DOI 0804
Remote sensing; Segmentation; QuickBird; Algorithms; Land cover BibRef

Tolpekin, V.A., Stein, A.,
Quantification of the Effects of Land-Cover-Class Spectral Separability on the Accuracy of Markov-Random-Field-Based Superresolution Mapping,
GeoRS(47), No. 9, September 2009, pp. 3283-3297.

Borengasser, M.[Marcus], Hungate, W.S.[William S.], Watkins, R.[Russell],
Hyperspectral Remote Sensing: Principles and Applications,
CRC PressDecember, 2007, ISBN: 9781566706544
WWW Link. Buy this book: Hyperspectral Remote Sensing 0910

Mather, P.[Paul], Tso, B.[Brandt], Bie-Tou,
Classification Methods for Remotely Sensed Data,
CRC PressMay 2009, ISBN: 9781420090727. Second Edition.
WWW Link. Buy this book: Classification Methods for Remotely Sensed Data, Second Edition 0910

Congalton, R.G.[Russell G.], Green, K.[Kass],
Assessing the Accuracy of Remotely Sensed Data: Principles and Practices,
CRC PressDecember, 2008, ISBN: 9781420055122
WWW Link. Buy this book: Assessing the Accuracy of Remotely Sensed Data: Principles and Practices, Second Edition (Mapping Science) 0910

Baraldi, A., Gironda, M., Simonetti, D.,
Operational Two-Stage Stratified Topographic Correction of Spaceborne Multispectral Imagery Employing an Automatic Spectral-Rule-Based Decision-Tree Preliminary Classifier,
GeoRS(48), No. 1, January 2010, pp. 112-146.

Forzieri, G., Castelli, F., Vivoni, E.R.,
A Predictive Multidimensional Model for Vegetation Anomalies Derived From Remote-Sensing Observations,
GeoRS(48), No. 4, April 2010, pp. 1729-1741.

Xie, Y.C.[Yi-Chun], Sha, Z.Y.[Zong-Yao], Bai, Y.F.[Yong-Fei],
Classifying historical remotely sensed imagery using a tempo-spatial feature evolution (T-SFE) model,
PandRS(65), No. 2, March 2010, pp. 182-190.
Elsevier DOI 1003
Classification; GIS; History; Landsat; Vegetation BibRef

Salberg, A.B.[Arnt-Břrre],
Land Cover Classification of Cloud-Contaminated Multitemporal High-Resolution Images,
GeoRS(49), No. 1, January 2011, pp. 377-387.

Liu, Q.H.[Qing-Hui], Kampffmeyer, M.[Michael], Jenssen, R.[Robert], Salberg, A.B.[Arnt-Břrre],
Dense Dilated Convolutions' Merging Network for Land Cover Classification,
GeoRS(58), No. 9, September 2020, pp. 6309-6320.
Semantics, Remote sensing, Image segmentation, Machine learning, Task analysis, Data models, very high-resolution (VHR) optical imagery BibRef

Liu, K.[Kimfung], Shi, W.Z.[Wen-Zhong], Zhang, H.[Hua],
A fuzzy topology-based maximum likelihood classification,
PandRS(66), No. 1, January 2011, pp. 103-114.
Elsevier DOI 1101
Fuzzy topology; Maximum likelihood classification (MLC); Thresholding; Remote sensing; Land cover mapping BibRef

Li, W., Guo, Q., Elkan, C.,
A Positive and Unlabeled Learning Algorithm for One-Class Classification of Remote-Sensing Data,
GeoRS(49), No. 2, February 2011, pp. 717-725.

Li, W., Guo, Q.,
A New Accuracy Assessment Method for One-Class Remote Sensing Classification,
GeoRS(52), No. 8, August 2014, pp. 4621-4632.
Accuracy BibRef

Baek, J., Kim, J.W., Lim, G.J., Lee, D.C.,
Electromagnetic Land Surface Classification Through Integration of Optical and Radar Remote Sensing Data,
GeoRS(49), No. 4, April 2011, pp. 1214-1222.

Jun, G., Ghosh, J.,
Spatially Adaptive Classification of Land Cover With Remote Sensing Data,
GeoRS(49), No. 7, July 2011, pp. 2662-2673.

Jun, G., Ghosh, J.,
Semisupervised Learning of Hyperspectral Data With Unknown Land-Cover Classes,
GeoRS(51), No. 1, January 2013, pp. 273-282.

Li, W.D.[Wei-Dong], Zhang, C.R.[Chuan-Rong],
A Markov Chain Geostatistical Framework for Land-Cover Classification With Uncertainty Assessment Based on Expert-Interpreted Pixels From Remotely Sensed Imagery,
GeoRS(49), No. 8, August 2011, pp. 2983-2992.

Nidamanuri, R.R.[Rama Rao], Zbell, B.[Bernd],
Use of field reflectance data for crop mapping using airborne hyperspectral image,
PandRS(66), No. 5, September 2011, pp. 683-691.
Elsevier DOI 1110
Field spectrometry; HyMAP; Hyperspectral remote sensing; Crop classification; Spectral library BibRef

Rodriguez-Galiano, V.F., Ghimire, B., Rogan, J., Chica-Olmo, M., Rigol-Sanchez, J.P.,
An assessment of the effectiveness of a random forest classifier for land-cover classification,
PandRS(67), No. 1, January 2012, pp. 93-104.
Elsevier DOI 1202
Remote sensing; Machine learning; Classification; Random forest; Land-cover; Landsat Thematic Mapper BibRef

Li, A.[Ainong], Jiang, J.G.[Jin-Gang], Bian, J.H.[Jin-Hu], Deng, W.[Wei],
Combining the matter element model with the associated function of probability transformation for multi-source remote sensing data classification in mountainous regions,
PandRS(67), No. 1, January 2012, pp. 80-92.
Elsevier DOI 1202
Remote sensing; Classification; Matter element model; Associated function; Mountainous region. Land cover integrating constraints and remote sensing information. BibRef

Yoshioka, H., Miura, T., Obata, K.,
Derivation of Relationships between Spectral Vegetation Indices from Multiple Sensors Based on Vegetation Isolines,
RS(4), No. 3, March 2012, pp. 583-597.
DOI Link 1204

Obata, K.[Kenta],
Sensitivity Analysis Method for Spectral Band Adjustment between Hyperspectral Sensors: A Case Study Using the CLARREO Pathfinder and HISUI,
RS(11), No. 11, 2019, pp. xx-yy.
DOI Link 1906

Dandois, J., Ellis, E.,
Remote Sensing of Vegetation Structure Using Computer Vision,
RS(2), No. 4, April 2010, pp. 1157-1176.
DOI Link 1203

Motohka, T., Nasahara, K., Oguma, H., Tsuchida, S.,
Applicability of Green-Red Vegetation Index for Remote Sensing of Vegetation Phenology,
RS(2), No. 10, October 2010, pp. 2369-2387.
DOI Link 1203

Neugebauer, N.[Nikolaus], Vuolo, F.[Francesco],
Crop Water Requirements on Regional Level using Remote Sensing Data: A Case Study in the Marchfeld Region,
PFG(2014), No. 5, 2014, pp. 369-381.
DOI Link 1411

Roscher, R.[Ribana], Förstner, W.[Wolfgang], Waske, B.[Björn],
I2VM: Incremental import vector machines,
IVC(30), No. 4-5, May 2012, pp. 263-278.
Elsevier DOI 1206
Earlier: A1, A3, A2:
Incremental import vector machines for large area land cover classification,
Import vector machines; Incremental learning; Concept-drifts BibRef

Roscher, R.[Ribana], Wenzel, S., Waske, B.[Björn],
Discriminative archetypal self-taught learning for multispectral landcover classification,
Markov processes BibRef

Roscher, R.[Ribana], Waske, B.[Björn],
Shapelet-Based Sparse Representation for Landcover Classification of Hyperspectral Images,
GeoRS(54), No. 3, March 2016, pp. 1623-1634.
Dictionaries BibRef

Yang, J.X.[Jing-Xue], Wang, Y.P.[Yun-Peng],
Classification of 10m-resolution SPOT data using a combined Bayesian Network Classifier-shape adaptive neighborhood method,
PandRS(72), No. 1, August 2012, pp. 36-45.
Elsevier DOI 1209
Bayesian Network Classifier; Shape adaptive neighborhood; Fisher optimal division; Maximum Likelihood Classifier; Remote sensing; Classification BibRef

Huo, H., Qing, J., Fang, T., Li, N.,
Land Cover Classification Using Local Softened Affine Hull,
GeoRS(50), No. 11, November 2012, pp. 4369-4383.

Vuolo, F., Atzberger, C.,
Exploiting the Classification Performance of Support Vector Machines with Multi-Temporal Moderate-Resolution Imaging Spectroradiometer (MODIS) Data in Areas of Agreement and Disagreement of Existing Land Cover Products,
RS(4), No. 10, October 2012, pp. 3143-3167.
DOI Link 1210

Verrelst, J., Romijn, E., Kooistra, L.,
Mapping Vegetation Density in a Heterogeneous River Floodplain Ecosystem Using Pointable CHRIS/PROBA Data,
RS(4), No. 9, September 2012, pp. 2866-2889.
DOI Link 1210

Pan, Y., Hu, T., Zhu, X., Zhang, J., Wang, X.,
Mapping Cropland Distributions Using a Hard and Soft Classification Model,
GeoRS(50), No. 11, November 2012, pp. 4301-4312.

Arnold, S.[Stephan],
Integration of remote sensing data in national and European spatial data infrastructures derivation of CORINE Land Cover data from the DLM-DE,
PFG(2009), No. 2, 2009, pp. 129-141.
WWW Link. 1211

Arnold, S.,
Digital Landscape Model DLM-DE: Deriving land cover information by integration of topographic reference data with remote sensing data,
PDF File. 0906

Waser, L.T.[Lars T.], Klonus, S.[Sascha], Ehlers, M.[Manfred], Küchler, M.[Meinrad], Jung, A.[András],
Potential of Digital Sensors for Land Cover and Tree Species Classifications A Case Study in the Framework of the DGPF-Project,
PFG(2010), No. 2, 2010, pp. 141-156.
WWW Link. 1211

Jung, A.[András], Götze, C.[Christian], Glässer, C.[Cornelia],
Overview of Experimental Setups in Spectroscopic Laboratory Measurements: The SpecTour Project,
PFG(2012), No. 4, 2012, pp. 433-442.
WWW Link. 1211

Buck, O.[Oliver], Peter, B.[Benedikt], Büker, C.[Cordt],
Zwei-skaliger Ansatz zur Aktualisierung landwirtschaftlicher Referenzkulissen (LPIS),
PFG(2011), No. 5, 2011, pp. 339-348.
WWW Link. 1211

Kersten, J.[Jens], Gähler, M.[Monika], Voigt, S.[Stefan],
A General Framework for Fast and Interactive Classification of Optical VHR Satellite Imagery Using Hierarchical and Planar Markov Random Fields,
PFG(2010), No. 6, 2010, pp. 439-449.
WWW Link. 1211

Moser, G., Serpico, S.B., Benediktsson, J.A.,
Land-Cover Mapping by Markov Modeling of Spatial-Contextual Information in Very-High-Resolution Remote Sensing Images,
PIEEE(100), No. 3, March 2013, pp. 631-651.

Moser, G., de Giorgi, A., Serpico, S.B.,
Multiresolution Supervised Classification of Panchromatic and Multispectral Images by Markov Random Fields and Graph Cuts,
GeoRS(54), No. 9, September 2016, pp. 5054-5070.
Markov processes BibRef

Barb, A.[Adrian], Kilicay-Ergin, N.[Nil],
Genetic Optimization for Associative Semantic Ranking Models of Satellite Images by Land Cover,
IJGI(2), No. 2, 2013, pp. 531-552.
DOI Link 1307

Hu, F.[Fan], Yang, W.[Wen], Chen, J.[Jiayu], Sun, H.[Hong],
Tile-Level Annotation of Satellite Images Using Multi-Level Max-Margin Discriminative Random Field,
RS(5), No. 5, 2013, pp. 2275-2291.
DOI Link 1307

Xu, J.B.[Jian Bo], Song, L.S.[Li Sheng], Zhong, D.F.[De Fu], Zhao, Z.Z.[Zhi Zhong], Zhao, K.[Kai],
Remote Sensing Image Classification Based on a Modified Self-organizing Neural Network with a Priori Knowledge,
Sensors(153), No. 6, June 2013, pp. 29-36.
HTML Version. 1307

Kasetkasem, T.[Teerasit], Rakwatin, P.[Preesan], Sirisommai, R.[Ratchawit], Eiumnoh, A.[Apisit],
A Joint Land Cover Mapping and Image Registration Algorithm Based on a Markov Random Field Model,
RS(5), No. 10, 2013, pp. 5089-5121.
DOI Link 1311

Zhu, X.L.[Xiao-Lin], Liu, D.[Desheng],
MAP-MRF Approach to Landsat ETM+ SLC-Off Image Classification,
GeoRS(52), No. 2, February 2014, pp. 1131-1141.
geophysical image processing BibRef

Luo, W.[Wang], Li, H.L.[Hong-Liang], Liu, G.H.[Guang-Hui], Zeng, L.Y.[Liao-Yuan],
Semantic Annotation of Satellite Images Using Author-Genre-Topic Model,
GeoRS(52), No. 2, February 2014, pp. 1356-1368.
feature extraction BibRef

Luo, B.[Bin], Zhang, L.P.[Liang-Pei],
Robust Autodual Morphological Profiles for the Classification of High-Resolution Satellite Images,
GeoRS(52), No. 2, February 2014, pp. 1451-1462.
artificial satellites BibRef

Marconcini, M., Fernandez-Prieto, D., Buchholz, T.,
Targeted Land-Cover Classification,
GeoRS(52), No. 7, July 2014, pp. 4173-4193.
Accuracy BibRef

Chiang, J.L., Liou, J.J., Wei, C., Cheng, K.S.,
A Feature-Space Indicator Kriging Approach for Remote Sensing Image Classification,
GeoRS(52), No. 7, July 2014, pp. 4046-4055.
Accuracy BibRef

Liu, M.W., Ozdogan, M., Zhu, X.,
Crop Type Classification by Simultaneous Use of Satellite Images of Different Resolutions,
GeoRS(52), No. 6, June 2014, pp. 3637-3649.
Agriculture BibRef

Voisin, A., Krylov, V.A., Moser, G., Serpico, S.B., Zerubia, J.B.,
Supervised Classification of Multisensor and Multiresolution Remote Sensing Images With a Hierarchical Copula-Based Approach,
GeoRS(52), No. 6, June 2014, pp. 3346-3358.
Data models BibRef

Wawrzaszek, A.[Anna], Aleksandrowicz, S.[Sebastian], Krupiski, M.[Michal], Drzewiecki, W.[Wojciech],
Influence of Image Filtering on Land Cover Classification when using Fractal and Multifractal Features,
PFG(2014), No. 2, April 2014, pp. 101-115.
DOI Link 1405

Tran, T.V.[Trung V.], Julian, J.P.[Jason P.], de Beurs, K.M.[Kirsten M.],
Land Cover Heterogeneity Effects on Sub-Pixel and Per-Pixel Classifications,
IJGI(3), No. 2, 2014, pp. 540-553.
DOI Link 1405

Ji, L.[Lei], Zhang, L.[Li], Rover, J.[Jennifer], Wylie, B.K.[Bruce K.], Chen, X.X.[Xue-Xia],
Geostatistical estimation of signal-to-noise ratios for spectral vegetation indices,
PandRS(96), No. 1, 2014, pp. 20-27.
Elsevier DOI 1410
Geostatistics BibRef

Gu, J.Y.[Jian-Yu], Congalton, R.G.[Russell G.], Pan, Y.Z.[Yao-Zhong],
The Impact of Positional Errors on Soft Classification Accuracy Assessment: A Simulation Analysis,
RS(7), No. 1, 2015, pp. 579-599.
DOI Link 1502

Chew, C.C., Small, E.E., Larson, K.M., Zavorotny, V.U.,
Vegetation Sensing Using GPS-Interferometric Reflectometry: Theoretical Effects of Canopy Parameters on Signal-to-Noise Ratio Data,
GeoRS(53), No. 5, May 2015, pp. 2755-2764.
Global Positioning System BibRef

Siegmann, B.[Bastian], Glässer, C.[Cornelia], Itzerott, S.[Sibylle], Neumann, C.[Carsten],
An Enhanced Classification Approach using Hyperspectral Image Data in Combination with in situ Spectral Measurements for the Mapping of Vegetation Communities,
PFG(2014), No. 6, 2014, pp. 523-533.
DOI Link 1503

Burai, P.[Péter], Deák, B.[Balázs], Valkó, O.[Orsolya], Tomor, T.[Tamás],
Classification of Herbaceous Vegetation Using Airborne Hyperspectral Imagery,
RS(7), No. 2, 2015, pp. 2046-2066.
DOI Link 1503

See, L.[Linda], Schepaschenko, D.[Dmitry], Lesiv, M.[Myroslava], McCallum, I.[Ian], Fritz, S.[Steffen], Comber, A.J.[Alexis J.], Perger, C.[Christoph], Schill, C.[Christian], Zhao, Y.Y.[Yuan-Yuan], Maus, V.[Victor], Siraj, M.A.[Muhammad Athar], Albrecht, F.[Franziska], Cipriani, A.[Anna], Vakolyuk, M.[Maryana], Garcia, A.[Alfredo], Rabia, A.H.[Ahmed H.], Singha, K.[Kuleswar], Marcarini, A.A.[Abel Alan], Kattenborn, T.[Teja], Hazarika, R.[Rubul], Schepaschenko, M.[Maria], van der Velde, M.[Marijn], Kraxner, F.[Florian], Obersteiner, M.[Michael],
Building a hybrid land cover map with crowdsourcing and geographically weighted regression,
PandRS(103), No. 1, 2015, pp. 48-56.
Elsevier DOI 1504
Land cover BibRef

Wu, X.C.[Xiao-Cui], Ju, W.M.[Wei-Min], Zhou, Y.[Yanlian], He, M.Z.[Ming-Zhu], Law, B.E.[Beverly E.], Black, T.A.[T. Andrew], Margolis, H.A.[Hank A.], Cescatti, A.[Alessandro], Gu, L.H.[Lian-Hong], Montagnani, L.[Leonardo], Noormets, A.[Asko], Griffis, T.J.[Timothy J.], Pilegaard, K.[Kim], Varlagin, A.[Andrej], Valentini, R.[Riccardo], Blanken, P.D.[Peter D.], Wang, S.Q.[Shao-Qiang], Wang, H.M.[Hui-Min], Han, S.J.[Shi-Jie], Yan, J.H.[Jun-Hua], Li, Y.N.[Ying-Nian], Zhou, B.B.[Bing-Bing], Liu, Y.[Yibo],
Performance of Linear and Nonlinear Two-Leaf Light Use Efficiency Models at Different Temporal Scales,
RS(7), No. 3, 2015, pp. 2238-2278.
DOI Link 1504
productivity of plants. BibRef

Hou, D.Y.[Dong-Yang], Chen, J.[Jun], Wu, H.[Hao], Li, S.N.[Song-Nian], Chen, F.[Fei], Zhang, W.W.[Wei-Wei],
Active Collection of Land Cover Sample Data from Geo-Tagged Web Texts,
RS(7), No. 5, 2015, pp. 5805-5827.
DOI Link 1506

Xing, H.Q.[Hua-Qiao], Chen, J.[Jun], Wu, H.[Hao], Hou, D.Y.[Dong-Yang],
A Web Service-Oriented Geoprocessing System for Supporting Intelligent Land Cover Change Detection,
IJGI(8), No. 1, 2019, pp. xx-yy.
DOI Link 1901

Yan, S.[Shuang], Jiang, L.M.[Ling-Mei], Chai, L.[Linna], Yang, J.T.[Jun-Tao], Kou, X.K.[Xiao-Kang],
Calibration of the L-MEB Model for Croplands in HiWATER Using PLMR Observation,
RS(7), No. 8, 2015, pp. 10878.
DOI Link 1509

Bachmann, M.[Martin], Makarau, A.[Aliaksei], Segl, K.[Karl], Richter, R.[Rudolf],
Estimating the Influence of Spectral and Radiometric Calibration Uncertainties on EnMAP Data Products: Examples for Ground Reflectance Retrieval and Vegetation Indices,
RS(7), No. 8, 2015, pp. 10689.
DOI Link 1509

Ishihara, M.[Mitsunori], Inoue, Y.[Yoshio], Ono, K.[Keisuke], Shimizu, M.[Mariko], Matsuura, S.[Shoji],
The Impact of Sunlight Conditions on the Consistency of Vegetation Indices in Croplands: Effective Usage of Vegetation Indices from Continuous Ground-Based Spectral Measurements,
RS(7), No. 10, 2015, pp. 14079.
DOI Link 1511

Zhao, F.[Feng], Guo, Y.Q.[Yi-Qing], Huang, Y.B.[Yan-Bo], Verhoef, W.[Wout], van der Tol, C.[Christiaan], Dai, B.[Bo], Liu, L.Y.[Liang-Yun], Zhao, H.J.[Hui-Jie], Liu, G.[Guang],
Quantitative Estimation of Fluorescence Parameters for Crop Leaves with Bayesian Inversion,
RS(7), No. 10, 2015, pp. 14179.
DOI Link 1511

Bue, B.D.[Brian D.], Thompson, D.R.[David R.], Sellar, R.G.[R. Glenn], Podest, E.V.[Erika V.], Eastwood, M.L.[Michael L.], Helmlinger, M.C.[Mark C.], McCubbin, I.B.[Ian B.], Morgan, J.D.[John D.],
Leveraging in-scene spectra for vegetation species discrimination with MESMA-MDA,
PandRS(108), No. 1, 2015, pp. 33-48.
Elsevier DOI 1511
Hyperspectral BibRef

Szulkin, M.[Marta], Zelazowski, P.[Przemyslaw], Marrot, P.[Pascal], Charmantier, A.[Anne],
Application of High Resolution Satellite Imagery to Characterize Individual-Based Environmental Heterogeneity in a Wild Blue Tit Population,
RS(7), No. 10, 2015, pp. 13319.
DOI Link 1511

Xie, Y.H.[Yan-Hui], Shi, J.C.[Jian-Cheng], Lei, Y.H.[Yong-Hui], Li, Y.Q.[Yun-Qing],
Modeling Microwave Emission from Short Vegetation-Covered Surfaces,
RS(7), No. 10, 2015, pp. 14099.
DOI Link 1511

Peng, J.J.[Jing-Jing], Fan, W.J.[Wen-Jie], Xu, X.[Xiru], Wang, L.Z.[Li-Zhao], Liu, Q.H.[Qin-Huo], Li, J.[Jvcai], Zhao, P.[Peng],
Estimating Crop Albedo in the Application of a Physical Model Based on the Law of Energy Conservation and Spectral Invariants,
RS(7), No. 11, 2015, pp. 15536.
DOI Link 1512

Wu, X.D.[Xiao-Dan], Xiao, Q.[Qing], Wen, J.G.[Jian-Guang], Liu, Q.A.[Qi-Ang], You, D.Q.[Dong-Qin], Dou, B.C.[Bao-Cheng], Tang, Y.[Yong], Li, X.W.[Xiao-Wen],
Optimal Nodes Selectiveness from WSN to Fit Field Scale Albedo Observation and Validation in Long Time Series in the Foci Experiment Areas, Heihe,
RS(7), No. 11, 2015, pp. 14757.
DOI Link 1512

Atoum, Y.[Yousef], Afridi, M.J.[Muhammad Jamal], Liu, X.M.[Xiao-Ming], McGrath, J.M.[J. Mitchell], Hanson, L.E.[Linda E.],
On developing and enhancing plant-level disease rating systems in real fields,
PR(53), No. 1, 2016, pp. 287-299.
Elsevier DOI 1602
CLS Rater BibRef

Luo, H.[Heng], Li, L.[Lin], Zhu, H.H.[Hai-Hong], Kuai, X.[Xi], Zhang, Z.J.[Zhi-Jun], Liu, Y.[Yu],
Land Cover Extraction from High Resolution ZY-3 Satellite Imagery Using Ontology-Based Method,
IJGI(5), No. 3, 2016, pp. 31.
DOI Link 1604

Sawada, Y., Tsutsui, H., Koike, T., Rasmy, M., Seto, R., Fujii, H.,
A Field Verification of an Algorithm for Retrieving Vegetation Water Content From Passive Microwave Observations,
GeoRS(54), No. 4, April 2016, pp. 2082-2095.
Land surface BibRef

Chang, T.[Tommy], Comandur, B.[Bharath], Park, J.[Johnny], Kak, A.C.[Avinash C.],
A variance-based Bayesian framework for improving Land-Cover classification through wide-area learning from large geographic regions,
CVIU(147), No. 1, 2016, pp. 3-22.
Elsevier DOI 1605

Sicre, C.M.[Claire Marais], Inglada, J.[Jordi], Fieuzal, R.[Rémy], Baup, F.[Frédéric], Valero, S.[Silvia], Cros, J.[Jérôme], Huc, M.[Mireille], Demarez, V.[Valérie],
Early Detection of Summer Crops Using High Spatial Resolution Optical Image Time Series,
RS(8), No. 7, 2016, pp. 591.
DOI Link 1608

Chen, Y.Y.[Yuan-Yuan], Wang, Q.F.[Quan-Fang], Wang, Y.L.[Yan-Long], Duan, S.B.[Si-Bo], Xu, M.Z.[Miao-Zhong], Li, Z.L.[Zhao-Liang],
A Spectral Signature Shape-Based Algorithm for Landsat Image Classification,
IJGI(5), No. 9, 2016, pp. 154.
DOI Link 1610
More than just the value at the point. BibRef

Wang, H.S.[He-Song], Jia, G.S.[Gen-Suo], Zhang, A.Z.[An-Zhi], Miao, C.[Chen],
Assessment of Spatial Representativeness of Eddy Covariance Flux Data from Flux Tower to Regional Grid,
RS(8), No. 9, 2016, pp. 742.
DOI Link 1610

Block, S.[Sebastián], González, E.J.[Edgar J.], Gallardo-Cruz, J.A.[J. Alberto], Fernández, A.[Ana], Solórzano, J.V.[Jonathan V.], Meave, J.A.[Jorge A.],
Using Google Earth Surface Metrics to Predict Plant Species Richness in a Complex Landscape,
RS(8), No. 10, 2016, pp. 865.
DOI Link 1609

Connette, K.J.L.[Katherine J. LaJeunesse], Connette, G.[Grant], Bernd, A.[Asja], Phyo, P.[Paing], Aung, K.H.[Kyaw Htet], Tun, Y.L.[Ye Lin], Thein, Z.M.[Zaw Min], Horning, N.[Ned], Leimgruber, P.[Peter], Songer, M.[Melissa],
Assessment of Mining Extent and Expansion in Myanmar Based on Freely-Available Satellite Imagery,
RS(8), No. 11, 2016, pp. 912.
DOI Link 1612

Volpi, M., Tuia, D.[Devis],
Dense Semantic Labeling of Subdecimeter Resolution Images With Convolutional Neural Networks,
GeoRS(55), No. 2, February 2017, pp. 881-893.
geophysical image processing BibRef

Tan, Q.Y.[Qiao-Yu], Liu, Y.[Yezi], Chen, X.[Xia], Yu, G.X.[Guo-Xian],
Multi-Label Classification Based on Low Rank Representation for Image Annotation,
RS(9), No. 2, 2017, pp. xx-yy.
DOI Link 1703

Yan, L.[Li], Zhu, R.X.[Rui-Xi], Mo, N.[Nan], Liu, Y.[Yi],
Improved Class-Specific Codebook with Two-Step Classification for Scene-Level Classification of High Resolution Remote Sensing Images,
RS(9), No. 3, 2017, pp. xx-yy.
DOI Link 1704

Yan, L.[Li], Zhu, R.X.[Rui-Xi], Liu, Y.[Yi], Mo, N.[Nan],
Scene Capture and Selected Codebook-Based Refined Fuzzy Classification of Large High-Resolution Images,
GeoRS(56), No. 7, July 2018, pp. 4178-4192.
feature extraction, fuzzy set theory, image classification, image representation, image resolution, image segmentation, selection of representative vocabularies BibRef

Rozenstein, O.[Offer], Adamowski, J.[Jan],
Linking Spaceborne and Ground Observations of Autumn Foliage Senescence in Southern Québec, Canada,
RS(9), No. 6, 2017, pp. xx-yy.
DOI Link 1706

Lu, X., Zheng, X., Yuan, Y.,
Remote Sensing Scene Classification by Unsupervised Representation Learning,
GeoRS(55), No. 9, September 2017, pp. 5148-5157.
spatial pyramid model, weighted deconvolution model, BibRef

Santara, A., Mani, K., Hatwar, P., Singh, A., Garg, A., Padia, K., Mitra, P.,
BASS Net: Band-Adaptive Spectral-Spatial Feature Learning Neural Network for Hyperspectral Image Classification,
GeoRS(55), No. 9, September 2017, pp. 5293-5301.
deep learning based land cover classification algorithms, BibRef

Marinoni, A., Iannelli, G.C., Gamba, P.,
An Information Theory-Based Scheme for Efficient Classification of Remote Sensing Data,
GeoRS(55), No. 10, October 2017, pp. 5864-5876.
feature extraction, remote sensing, BibRef

Liu, Q., Hang, R., Song, H., Li, Z.,
Learning Multiscale Deep Features for High-Resolution Satellite Image Scene Classification,
GeoRS(56), No. 1, January 2018, pp. 117-126.
Feature extraction, Histograms, Learning systems, Satellites, Spatial resolution, Training, Visualization, spatial pyramid pooling BibRef

Chen, W.T.[Wei-Tao], Li, X.J.[Xian-Ju], He, H.X.[Hai-Xia], Wang, L.Z.[Li-Zhe],
Assessing Different Feature Sets' Effects on Land Cover Classification in Complex Surface-Mined Landscapes by ZiYuan-3 Satellite Imagery,
RS(10), No. 1, 2018, pp. xx-yy.
DOI Link 1802

Svendsen, D.H.[Daniel H.], Martino, L.[Luca], Campos-Taberner, M.[Manuel], Garcia-Haro, F.J., Camps-Valls, G.[Gustau],
Joint Gaussian Processes for Biophysical Parameter Retrieval,
GeoRS(56), No. 3, March 2018, pp. 1718-1727.
Gaussian processes, geophysical image processing, inverse problems, learning (artificial intelligence), vegetation monitoring BibRef

Camps-Valls, G.[Gustau], Svendsen, D.H.[Daniel H.], Martino, L.[Luca], Muńoz-Marí, J.[Jordi], Laparra, V.[Valero], Campos-Taberner, M.[Manuel], Luengo, D.[David],
Physics-Aware Gaussian Processes for Earth Observation,
SCIA17(II: 205-217).
Springer DOI 1706

Zhang, X.N.[Xiao-Ning], Jiao, Z.[Ziti], Dong, Y.D.[Ya-Dong], Zhang, H.[Hu], Li, Y.[Yang], He, D.D.[Dan-Dan], Ding, A.X.[An-Xin], Yin, S.Y.[Si-Yang], Cui, L.[Lei], Chang, Y.X.[Ya-Xuan],
Potential Investigation of Linking PROSAIL with the Ross-Li BRDF Model for Vegetation Characterization,
RS(10), No. 3, 2018, pp. xx-yy.
DOI Link 1804

Guo, R.[Rui], Liu, J.B.[Jian-Bo], Li, N.[Na], Liu, S.B.[Shi-Bin], Chen, F.[Fu], Cheng, B.[Bo], Duan, J.B.[Jian-Bo], Li, X.P.[Xin-Peng], Ma, C.H.[Cai-Hong],
Pixel-Wise Classification Method for High Resolution Remote Sensing Imagery Using Deep Neural Networks,
IJGI(7), No. 3, 2018, pp. xx-yy.
DOI Link 1804

Xia, W.[Wei], Ma, C.H.[Cai-Hong], Liu, J.B.[Jian-Bo], Liu, S.B.[Shi-Bin], Chen, F.[Fu], Yang, Z.[Zhi], Duan, J.B.[Jian-Bo],
High-Resolution Remote Sensing Imagery Classification of Imbalanced Data Using Multistage Sampling Method and Deep Neural Networks,
RS(11), No. 21, 2019, pp. xx-yy.
DOI Link 1911

Wen, J.G.[Jian-Guang], Liu, Q.A.[Qi-Ang], Xiao, Q.[Qing], Liu, Q.H.[Qin-Huo], You, D.Q.[Dong-Qin], Hao, D.L.[Da-Lei], Wu, S.B.[Sheng-Biao], Lin, X.W.[Xing-Wen],
Characterizing Land Surface Anisotropic Reflectance over Rugged Terrain: A Review of Concepts and Recent Developments,
RS(10), No. 3, 2018, pp. xx-yy.
DOI Link 1804

Hao, D.L.[Da-Lei], Wen, J.G.[Jian-Guang], Xiao, Q.[Qing], Wu, S.B.[Sheng-Biao], Lin, X.W.[Xing-Wen], Dou, B.C.[Bao-Cheng], You, D.Q.[Dong-Qin], Tang, Y.[Yong],
Simulation and Analysis of the Topographic Effects on Snow-Free Albedo over Rugged Terrain,
RS(10), No. 2, 2018, pp. xx-yy.
DOI Link 1804

Wu, S.B.[Sheng-Biao], Wen, J.G.[Jian-Guang], Lin, X.W.[Xing-Wen], Hao, D.L.[Da-Lei], You, D.Q.[Dong-Qin], Xiao, Q.[Qing], Liu, Q.H.[Qin-Huo], Yin, T.G.[Tian-Gang],
Modeling Discrete Forest Anisotropic Reflectance Over a Sloped Surface With an Extended GOMS and SAIL Model,
GeoRS(57), No. 2, February 2019, pp. 944-957.
Surface topography, Atmospheric modeling, Forestry, Vegetation, Scattering, Remote sensing, Canopy reflectance, sloped surface BibRef

Marcinkowska-Ochtyra, A.[Adriana], Zagajewski, B.[Bogdan], Raczko, E.[Edwin], Ochtyra, A.[Adrian], Jarocinska, A.[Anna],
Classification of High-Mountain Vegetation Communities within a Diverse Giant Mountains Ecosystem Using Airborne APEX Hyperspectral Imagery,
RS(10), No. 4, 2018, pp. xx-yy.
DOI Link 1805

Costa, H.[Hugo], Almeida, D.[Diana], Vala, F.[Francisco], Marcelino, F.[Filipe], Caetano, M.[Mário],
Land Cover Mapping from Remotely Sensed and Auxiliary Data for Harmonized Official Statistics,
IJGI(7), No. 4, 2018, pp. xx-yy.
DOI Link 1805

Hao, Y.L.[Yan-Ling], Cui, T.W.[Ting-Wei], Singh, V.P.[Vijay P.], Zhang, J.[Jie], Yu, R.H.[Rui-Hong], Zhao, W.J.[Wen-Jing],
Diurnal Variation of Light Absorption in the Yellow River Estuary,
RS(10), No. 4, 2018, pp. xx-yy.
DOI Link 1805

McRoberts, R.E.[Ronald E.], Stehman, S.V.[Stephen V.], Liknes, G.C.[Greg C.], Nćsset, E.[Erik], Sannier, C.[Christophe], Walters, B.F.[Brian F.],
The effects of imperfect reference data on remote sensing-assisted estimators of land cover class proportions,
PandRS(142), 2018, pp. 292-300.
Elsevier DOI 1807
Intepreter error, Bias, Precision, Greenhouse gas inventory, Gain-loss method BibRef

Mahdianpari, M.[Masoud], Salehi, B.[Bahram], Rezaee, M.[Mohammad], Mohammadimanesh, F.[Fariba], Zhang, Y.[Yun],
Very Deep Convolutional Neural Networks for Complex Land Cover Mapping Using Multispectral Remote Sensing Imagery,
RS(10), No. 7, 2018, pp. xx-yy.
DOI Link 1808

Yang, G., Shen, H., Sun, W., Li, J., Diao, N., He, Z.,
On the Generation of Gapless and Seamless Daily Surface Reflectance Data,
GeoRS(56), No. 8, August 2018, pp. 4289-4306.
geophysical image processing, image reconstruction, land cover, remote sensing, time series, time series BibRef

Yu, Y.L.[Yun-Long], Liu, F.X.[Fu-Xian],
Dense Connectivity Based Two-Stream Deep Feature Fusion Framework for Aerial Scene Classification,
RS(10), No. 7, 2018, pp. xx-yy.
DOI Link 1808

Levy, C.R.[Charlotte R.], Burakowski, E.[Elizabeth], Richardson, A.D.[Andrew D.],
Novel Measurements of Fine-Scale Albedo: Using a Commercial Quadcopter to Measure Radiation Fluxes,
RS(10), No. 8, 2018, pp. xx-yy.
DOI Link 1809

Sun, H.[Hua], Wang, Q.[Qing], Wang, G.X.[Guang-Xing], Lin, H.[Hui], Luo, P.[Peng], Li, J.P.[Ji-Ping], Zeng, S.Q.[Si-Qi], Xu, X.Y.[Xiao-Yu], Ren, L.X.[Lan-Xiang],
Optimizing kNN for Mapping Vegetation Cover of Arid and Semi-Arid Areas Using Landsat Images,
RS(10), No. 8, 2018, pp. xx-yy.
DOI Link 1809

Garcia-Salgado, B.P.[Beatriz P.], Ponomaryov, V.I.[Volodymyr I.], Sadovnychiy, S.[Sergiy], Robles-Gonzalez, M.[Marco],
Parallel supervised land-cover classification system for hyperspectral and multispectral images,
RealTimeIP(14), No. 3, October 2018, pp. 687-704.
Springer DOI 1811

Zeng, Y.[Yelu], Xu, B.D.[Bao-Dong], Yin, G.F.[Gao-Fei], Wu, S.B.[Sheng-Biao], Hu, G.Q.[Guo-Qing], Yan, K.[Kai], Yang, B.[Bin], Song, W.J.[Wan-Juan], Li, J.[Jing],
Spectral Invariant Provides a Practical Modeling Approach for Future Biophysical Variable Estimations,
RS(10), No. 10, 2018, pp. xx-yy.
DOI Link 1811

Campos, J.C.[Joăo Carlos], Brito, J.C.[José Carlos],
Mapping underrepresented land cover heterogeneity in arid regions: The Sahara-Sahel example,
PandRS(146), 2018, pp. 211-220.
Elsevier DOI 1812
Arid regions, Ecoregions, Landsat, Remote sensing, Supervised classification BibRef

Zhao, W.Z.[Wen-Zhi], Emery, W.J.[William J.], Bo, Y.C.[Yan-Chen], Chen, J.G.[Jia-Ge],
Land Cover Mapping with Higher Order Graph-Based Co-Occurrence Model,
RS(10), No. 11, 2018, pp. xx-yy.
DOI Link 1812

Gaetano, R.[Raffaele], Ienco, D.[Dino], Ose, K.[Kenji], Cresson, R.[Remi],
A Two-Branch CNN Architecture for Land Cover Classification of PAN and MS Imagery,
RS(10), No. 11, 2018, pp. xx-yy.
DOI Link 1812

Zhang, W.[Wei], Tang, P.[Ping], Zhao, L.J.[Li-Jun],
Remote Sensing Image Scene Classification Using CNN-CapsNet,
RS(11), No. 5, 2019, pp. xx-yy.
DOI Link 1903

da Re, D.[Daniele], de Clercq, E.M.[Eva M.], Tordoni, E.[Enrico], Madder, M.[Maxime], Rousseau, R.[Raphaël], Vanwambeke, S.O.[Sophie O.],
Looking for Ticks from Space: Using Remotely Sensed Spectral Diversity to Assess Amblyomma and Hyalomma Tick Abundance,
RS(11), No. 7, 2019, pp. xx-yy.
DOI Link 1904

Park, S.E.[Sang-Eun], Jung, Y.T.[Yoon Taek], Cho, J.H.[Jae-Hyoung], Moon, H.[Hyoi], Han, S.H.[Seung-Hoon],
Theoretical Evaluation of Water Cloud Model Vegetation Parameters,
RS(11), No. 8, 2019, pp. xx-yy.
DOI Link 1905
For scattering. BibRef

Muhammad, U.[Usman], Wang, W.Q.A.[Wei-Qi-Ang], Hadid, A.[Abdenour], Pervez, S.[Shahbaz],
Bag of words KAZE (BoWK) with two-step classification for high-resolution remote sensing images,
IET-CV(13), No. 4, June 2019, pp. 395-403.
DOI Link 1906

See also KAZE Features. BibRef

Ratajczak, R., Crispim-Junior, C.F., Faure, E., Fervers, B., Tougne, L.,
Automatic Land Cover Reconstruction From Historical Aerial Images: An Evaluation of Features Extraction and Classification Algorithms,
IP(28), No. 7, July 2019, pp. 3357-3371.
computer vision, convolutional neural nets, feature extraction, filtering theory, geophysical image processing, historical aerial images BibRef

Gewali, U.B.[Utsav B.], Monteiro, S.T.[Sildomar T.], Saber, E.[Eli],
Gaussian Processes for Vegetation Parameter Estimation from Hyperspectral Data with Limited Ground Truth,
RS(11), No. 13, 2019, pp. xx-yy.
DOI Link 1907

Stanimirova, R.[Radost], Cai, Z.Z.[Zhan-Zhang], Melaas, E.K.[Eli K.], Gray, J.M.[Josh M.], Eklundh, L.[Lars], Jönsson, P.[Per], Friedl, M.A.[Mark A.],
An Empirical Assessment of the MODIS Land Cover Dynamics and TIMESAT Land Surface Phenology Algorithms,
RS(11), No. 19, 2019, pp. xx-yy.
DOI Link 1910

Stewart, E.L.[Ethan L.], Wiesner-Hanks, T.[Tyr], Kaczmar, N.[Nicholas], DeChant, C.[Chad], Wu, H.[Harvey], Lipson, H.[Hod], Nelson, R.J.[Rebecca J.], Gore, M.A.[Michael A.],
Quantitative Phenotyping of Northern Leaf Blight in UAV Images Using Deep Learning,
RS(11), No. 19, 2019, pp. xx-yy.
DOI Link 1910

Petliak, H.[Helen], Cerovski-Darriau, C.[Corina], Zaliva, V.[Vadim], Stock, J.[Jonathan],
Where's the Rock: Using Convolutional Neural Networks to Improve Land Cover Classification,
RS(11), No. 19, 2019, pp. xx-yy.
DOI Link 1910

Fang, J.[Jie], Yuan, Y.[Yuan], Lu, X.Q.[Xiao-Qiang], Feng, Y.C.[Ya-Chuang],
Robust Space-Frequency Joint Representation for Remote Sensing Image Scene Classification,
GeoRS(57), No. 10, October 2019, pp. 7492-7502.
convolutional neural nets, feature extraction, geophysical image processing, image classification, space domain BibRef

Silver, M.[Micha], Tiwari, A.[Arti], Karnieli, A.[Arnon],
Identifying Vegetation in Arid Regions Using Object-Based Image Analysis with RGB-Only Aerial Imagery,
RS(11), No. 19, 2019, pp. xx-yy.
DOI Link 1910

Fang, B.[Bo], Kou, R.[Rong], Pan, L.[Li], Chen, P.F.[Peng-Fei],
Category-Sensitive Domain Adaptation for Land Cover Mapping in Aerial Scenes,
RS(11), No. 22, 2019, pp. xx-yy.
DOI Link 1911

Ma, A.L.[Ai-Long], Wan, Y.T.[Yu-Ting], Zhong, Y.F.[Yan-Fei], Wang, J.J.[Jun-Jue], Zhang, L.P.[Liang-Pei],
SceneNet: Remote sensing scene classification deep learning network using multi-objective neural evolution architecture search,
PandRS(172), 2021, pp. 171-188.
Elsevier DOI 2101
Scene classification, deep neural network, remote sensing, multi-objective optimization, evolutionary algorithm, neural architecture search BibRef

Blanco-Sacristán, J.[Javier], Panigada, C.[Cinzia], Tagliabue, G.[Giulia], Gentili, R.[Rodolfo], Colombo, R.[Roberto], Ladrón de Guevara, M.[Mónica], Maestre, F.T.[Fernando T.], Rossini, M.[Micol],
Spectral Diversity Successfully Estimates the a-Diversity of Biocrust-Forming Lichens,
RS(11), No. 24, 2019, pp. xx-yy.
DOI Link 1912

Alonso-Sarria, F.[Francisco], Valdivieso-Ros, C.[Carmen], Gomariz-Castillo, F.[Francisco],
Isolation Forests to Evaluate Class Separability and the Representativeness of Training and Validation Areas in Land Cover Classification,
RS(11), No. 24, 2019, pp. xx-yy.
DOI Link 1912

Li, X.D.[Xiao-Dong], Chen, R.[Rui], Foody, G.M.[Giles M.], Wang, L.H.[Li-Hui], Yang, X.H.[Xiao-Hong], Du, Y.[Yun], Ling, F.[Feng],
Spatio-Temporal Sub-Pixel Land Cover Mapping of Remote Sensing Imagery Using Spatial Distribution Information From Same-Class Pixels,
RS(12), No. 3, 2020, pp. xx-yy.
DOI Link 2002

Lei, G.B.[Guang-Bin], Li, A.N.[Ai-Nong], Bian, J.H.[Jin-Hu], Yan, H.[He], Zhang, L.[Lulu], Zhang, Z.J.[Zheng-Jian], Nan, X.[Xi],
OIC-MCE: A Practical Land Cover Mapping Approach for Limited Samples Based on Multiple Classifier Ensemble and Iterative Classification,
RS(12), No. 6, 2020, pp. xx-yy.
DOI Link 2003

Hou, W.J.[Wen-Juan], Gao, J.B.[Jiang-Bo],
Spatially Variable Relationships between Karst Landscape Pattern and Vegetation Activities,
RS(12), No. 7, 2020, pp. xx-yy.
DOI Link 2004

Ma, X.L.[Xuan-Long], Migliavacca, M.[Mirco], Wirth, C.[Christian], Bohn, F.J.[Friedrich J.], Huth, A.[Andreas], Richter, R.[Ronny], Mahecha, M.D.[Miguel D.],
Monitoring Plant Functional Diversity Using the Reflectance and Echo from Space,
RS(12), No. 8, 2020, pp. xx-yy.
DOI Link 2004

Laamrani, A.[Ahmed], Joosse, P.[Pamela], McNairn, H.[Heather], Berg, A.A.[Aaron A.], Hagerman, J.[Jennifer], Powell, K.[Kathryn], Berry, M.[Mark],
Assessing Soil Cover Levels during the Non-Growing Season Using Multitemporal Satellite Imagery and Spectral Unmixing Techniques,
RS(12), No. 9, 2020, pp. xx-yy.
DOI Link 2005

Chen, C.P.J.[Chun-Peng James], Zhang, Z.W.[Zhi-Wu],
GRID: A Python Package for Field Plot Phenotyping Using Aerial Images,
RS(12), No. 11, 2020, pp. xx-yy.
DOI Link 2006

Zhai, R.T.[Rui-Ting], Zhang, C.R.[Chuan-Rong], Li, W.D.[Wei-Dong], Zhang, X.[Xiang], Li, X.[Xueke],
Evaluation of Driving Forces of Land Use and Land Cover Change in New England Area by a Mixed Method,
IJGI(9), No. 6, 2020, pp. xx-yy.
DOI Link 2006

Kwan, C.[Chiman], Ayhan, B.[Bulent], Budavari, B.[Bence], Lu, Y.[Yan], Perez, D.[Daniel], Li, J.[Jiang], Bernabe, S.[Sergio], Plaza, A.[Antonio],
Deep Learning for Land Cover Classification Using Only a Few Bands,
RS(12), No. 12, 2020, pp. xx-yy.
DOI Link 2006

Kwan, C.[Chiman], Gribben, D.[David], Ayhan, B.[Bulent], Li, J.[Jiang], Bernabe, S.[Sergio], Plaza, A.[Antonio],
An Accurate Vegetation and Non-Vegetation Differentiation Approach Based on Land Cover Classification,
RS(12), No. 23, 2020, pp. xx-yy.
DOI Link 2012

Chivasa, W.[Walter], Mutanga, O.[Onisimo], Biradar, C.[Chandrashekhar],
UAV-Based Multispectral Phenotyping for Disease Resistance to Accelerate Crop Improvement under Changing Climate Conditions,
RS(12), No. 15, 2020, pp. xx-yy.
DOI Link 2008

Sakuma, A.[Asahi], Yamano, H.[Hiroya],
Satellite Constellation Reveals Crop Growth Patterns and Improves Mapping Accuracy of Cropping Practices for Subtropical Small-Scale Fields in Japan,
RS(12), No. 15, 2020, pp. xx-yy.
DOI Link 2008

Blanco, S.R.[Sergio R.], Heras, D.B.[Dora B.], Argüello, F.[Francisco],
Texture Extraction Techniques for the Classification of Vegetation Species in Hyperspectral Imagery: Bag of Words Approach Based on Superpixels,
RS(12), No. 16, 2020, pp. xx-yy.
DOI Link 2008

Mboga, N.[Nicholus], Grippa, T.[Tais], Georganos, S.[Stefanos], Vanhuysse, S.[Sabine], Smets, B.[Benoît], Dewitte, O.[Olivier], Wolff, E.[Elčonore], Lennert, M.[Moritz],
Fully convolutional networks for land cover classification from historical panchromatic aerial photographs,
PandRS(167), 2020, pp. 385-395.
Elsevier DOI 2008
Fully convolutional networks, Deep learning, Panchromatic historical aerial imagery, Land cover classification BibRef

Park, J.[Jinseok], Jang, S.[Seongju], Hong, R.[Rokgi], Suh, K.[Kyo], Song, I.[Inhong],
Development of Land Cover Classification Model Using AI Based FusionNet Network,
RS(12), No. 19, 2020, pp. xx-yy.
DOI Link 2010

Samarinas, N.[Nikiforos], Tziolas, N.[Nikolaos], Zalidis, G.[George],
Improved Estimations of Nitrate and Sediment Concentrations Based on SWAT Simulations and Annual Updated Land Cover Products from a Deep Learning Classification Algorithm,
IJGI(9), No. 10, 2020, pp. xx-yy.
DOI Link 2010

Thomas, N.[Nathan], Neigh, C.S.R.[Christopher S. R.], Carroll, M.L.[Mark L.], McCarty, J.L.[Jessica L.], Bunting, P.[Pete],
Fusion Approach for Remotely-Sensed Mapping of Agriculture (FARMA): A Scalable Open Source Method for Land Cover Monitoring Using Data Fusion,
RS(12), No. 20, 2020, pp. xx-yy.
DOI Link 2010

Gudmann, A.[András], Csikós, N.[Nándor], Szilassi, P.[Péter], Mucsi, L.[László],
Improvement in Satellite Image-Based Land Cover Classification with Landscape Metrics,
RS(12), No. 21, 2020, pp. xx-yy.
DOI Link 2011

Radke, D.[David], Radke, D.[Daniel], Radke, J.[John],
Beyond Measurement: Extracting Vegetation Height from High Resolution Imagery with Deep Learning,
RS(12), No. 22, 2020, pp. xx-yy.
DOI Link 2011

Baudoux, L.[Luc], Inglada, J.[Jordi], Mallet, C.[Clément],
Toward a Yearly Country-Scale CORINE Land-Cover Map without Using Images: A Map Translation Approach,
RS(13), No. 6, 2021, pp. xx-yy.
DOI Link 2104

Minghelli, A.[Audrey], Chevalier, C.[Cristele], Descloitres, J.[Jacques], Berline, L.[Léo], Blanc, P.[Philippe], Chami, M.[Malik],
Synergy between Low Earth Orbit (LEO): MODIS and Geostationary Earth Orbit (GEO): GOES Sensors for Sargassum Monitoring in the Atlantic Ocean,
RS(13), No. 8, 2021, pp. xx-yy.
DOI Link 2104

Li, X.[Xiao], Lei, L.[Lin], Sun, Y.[Yuli], Li, M.[Ming], Kuang, G.Y.[Guang-Yao],
Collaborative Attention-Based Heterogeneous Gated Fusion Network for Land Cover Classification,
GeoRS(59), No. 5, May 2021, pp. 3829-3845.
Optical imaging, Feature extraction, Logic gates, Nonlinear optics, Synthetic aperture radar, Collaboration, Optical sensors, land cover classification BibRef

Li, X.[Xiao], Lei, L.[Lin], Sun, Y.[Yuli], Kuang, G.Y.[Gang-Yao],
Dynamic-Hierarchical Attention Distillation With Synergetic Instance Selection for Land Cover Classification Using Missing Heterogeneity Images,
GeoRS(60), 2022, pp. 1-16.
Training, Optical imaging, Nonhomogeneous media, Synthetic aperture radar, Optical sensors, Streaming media, privileged information BibRef

Ma, D.D.[Dong-Dong], Rehman, T.U.[Tanzeel U.], Zhang, L.[Libo], Maki, H.[Hideki], Tuinstra, M.R.[Mitchell R.], Jin, J.[Jian],
Modeling of Diurnal Changing Patterns in Airborne Crop Remote Sensing Images,
RS(13), No. 9, 2021, pp. xx-yy.
DOI Link 2105

Levering, A.[Alex], Marcos, D.[Diego], Tuia, D.[Devis],
On the relation between landscape beauty and land cover: A case study in the U.K. at Sentinel-2 resolution with interpretable AI,
PandRS(177), 2021, pp. 194-203.
Elsevier DOI 2106
Landscape aesthetics, Deep learning, Interpretable AI, Corine land cover, Sentinel-2 BibRef

Jozdani, S.[Shahab], Chen, D.M.[Dong-Mei], Chen, W.J.[Wen-Jun], Leblanc, S.G.[Sylvain G.], Prévost, C.[Christian], Lovitt, J.[Julie], He, L.M.[Li-Ming], Johnson, B.A.[Brian A.],
Leveraging Deep Neural Networks to Map Caribou Lichen in High-Resolution Satellite Images Based on a Small-Scale, Noisy UAV-Derived Map,
RS(13), No. 14, 2021, pp. xx-yy.
DOI Link 2107

Bui, Q.T.[Quang-Thanh], Chou, T.Y.[Tien-Yin], Hoang, T.V.[Thanh-Van], Fang, Y.M.[Yao-Min], Mu, C.Y.[Ching-Yun], Huang, P.H.[Pi-Hui], Pham, V.D.[Vu-Dong], Nguyen, Q.H.[Quoc-Huy], Anh, D.T.N.[Do Thi Ngoc], Pham, V.M.[Van-Manh], Meadows, M.E.[Michael E.],
Gradient Boosting Machine and Object-Based CNN for Land Cover Classification,
RS(13), No. 14, 2021, pp. xx-yy.
DOI Link 2107

Sainos-Vizuett, M.[Michelle], Lopez-Nava, I.H.[Irvin Hussein],
Satellite Imagery Classification Using Shallow and Deep Learning Approaches,
Springer DOI 2108

Zhang, J.R.[Jian-Rong], Zhao, H.W.[Hong-Wei], Li, J.[Jiao],
TRS: Transformers for Remote Sensing Scene Classification,
RS(13), No. 20, 2021, pp. xx-yy.
DOI Link 2110
pure CNNs -> Convolution + Transformer -> pure Transformers BibRef

Jozdani, S.[Shahab], Chen, D.M.[Dong-Mei], Chen, W.J.[Wen-Jun], Leblanc, S.G.[Sylvain G.], Lovitt, J.[Julie], He, L.M.[Li-Ming], Fraser, R.H.[Robert H.], Johnson, B.A.[Brian Alan],
Evaluating Image Normalization via GANs for Environmental Mapping: A Case Study of Lichen Mapping Using High-Resolution Satellite Imagery,
RS(13), No. 24, 2021, pp. xx-yy.
DOI Link 2112

Huang, A.[Anqi], Shen, R.[Runping], Li, Y.Q.[Ye-Qing], Han, H.M.[Hui-Min], Di, W.L.[Wen-Li], Hagan, D.F.T.[Daniel Fiifi Tawia],
A Methodology to Generate Integrated Land Cover Data for Land Surface Model by Improving Dempster-Shafer Theory,
RS(14), No. 4, 2022, pp. xx-yy.
DOI Link 2202

Jiang, H.[Hong], Yao, M.L.[Mao-Lin], Guo, J.[Jia], Zhang, Z.M.[Zhao-Ming], Wu, W.T.[Wen-Ting], Mao, Z.Y.[Zheng-Yuan],
Vegetation Monitoring of Protected Areas in Rugged Mountains Using an Improved Shadow-Eliminated Vegetation Index (SEVI),
RS(14), No. 4, 2022, pp. xx-yy.
DOI Link 2202

Li, Y.S.[Yan-Sheng], Zhou, Y.H.[Yu-Han], Zhang, Y.J.[Yong-Jun], Zhong, L.H.[Li-Heng], Wang, J.[Jian], Chen, J.D.[Jing-Dong],
DKDFN: Domain Knowledge-Guided deep collaborative fusion network for multimodal unitemporal remote sensing land cover classification,
PandRS(186), 2022, pp. 170-189.
Elsevier DOI 2203
Land cover classification, Deep collaborative network, Domain knowledge incorporation, Multimodal unitemporal remote sensing BibRef

Liu, Z.Y.C.[Zac Yung-Chun], Chamberlin, A.J.[Andrew J.], Tallam, K.[Krti], Jones, I.J.[Isabel J.], Lamore, L.L.[Lance L.], Bauer, J.[John], Bresciani, M.[Mariano], Wolfe, C.M.[Caitlin M.], Casagrandi, R.[Renato], Mari, L.[Lorenzo], Gatto, M.[Marino], Diongue, A.K.[Abdou Ka], Toure, L.[Lamine], Rohr, J.R.[Jason R.], Riveau, G.[Gilles], Jouanard, N.[Nicolas], Wood, C.L.[Chelsea L.], Sokolow, S.H.[Susanne H.], Mandle, L.[Lisa], Daily, G.[Gretchen], Lambin, E.F.[Eric F.], Leo, G.A.D.[Giulio A. De],
Deep Learning Segmentation of Satellite Imagery Identifies Aquatic Vegetation Associated with Snail Intermediate Hosts of Schistosomiasis in Senegal, Africa,
RS(14), No. 6, 2022, pp. xx-yy.
DOI Link 2204
Bilharzia Or Snail Fever. Water-borne parasite. BibRef

Wang, D.[Di], Yang, R.H.[Rong-Hao], Liu, H.[Hanhu], He, H.Q.[Hai-Qing], Tan, J.X.[Jun-Xiang], Li, S.[Shaoda], Qiao, Y.C.[Yi-Chun], Tang, K.Q.[Kang-Qi], Wang, X.[Xiao],
HFENet: Hierarchical Feature Extraction Network for Accurate Landcover Classification,
RS(14), No. 17, 2022, pp. xx-yy.
DOI Link 2209

Ma, W.L.[Wan-Li], Karakus, O.[Oktay], Rosin, P.L.[Paul L.],
AMM-FuseNet: Attention-Based Multi-Modal Image Fusion Network for Land Cover Mapping,
RS(14), No. 18, 2022, pp. xx-yy.
DOI Link 2209

Schamberger, L.[Léa], Minghelli, A.[Audrey], Chami, M.[Malik],
Quantification of Underwater Sargassum Aggregations Based on a Semi-Analytical Approach Applied to Sentinel-3/OLCI (Copernicus) Data in the Tropical Atlantic Ocean,
RS(14), No. 20, 2022, pp. xx-yy.
DOI Link 2211

Ye, Y.C.[Yong-Chang], Zhang, X.Y.[Xiao-Yang], Shen, Y.[Yu], Wang, J.M.[Jian-Min], Crimmins, T.[Theresa], Scheifinger, H.[Helfried],
An optimal method for validating satellite-derived land surface phenology using in-situ observations from national phenology networks,
PandRS(194), 2022, pp. 74-90.
Elsevier DOI 2212
Phenology, In-situ observations, PEP725, USA-NPN, VIIRS, LSP validation BibRef

van den Broeck, W.A.J.[Wouter A. J.], Goedemé, T.[Toon], Loopmans, M.[Maarten],
Multiclass Land Cover Mapping from Historical Orthophotos Using Domain Adaptation and Spatio-Temporal Transfer Learning,
RS(14), No. 23, 2022, pp. xx-yy.
DOI Link 2212

Xing, H.Q.[Hua-Qiao], Liu, C.[Chang], Li, R.[Rui], Wang, H.H.[Hai-Hang], Zhang, J.H.[Jin-Hua], Wu, H.Y.[Hua-Yi],
Domain Constraints-Driven Automatic Service Composition for Online Land Cover Geoprocessing,
IJGI(11), No. 12, 2022, pp. xx-yy.
DOI Link 2301

Wu, H.[Hao], Chen, J.[Jun], Xing, H.Q.[Hua-Qiao], Li, S.[Songnian], Hu, J.[Juju],
Pragmatics Driven Land Cover Service Composition Utilizing Behavior-intention Model,
ISPRS16(B8: 1319-1325).
DOI Link 1610

Hu, K.[Kai], Zhang, E.[Enwei], Dai, X.[Xin], Xia, M.[Min], Zhou, F.H.[Feng-Hua], Weng, L.G.[Li-Guo], Lin, H.F.[Hai-Feng],
MCSGNet: A Encoder-Decoder Architecture Network for Land Cover Classification,
RS(15), No. 11, 2023, pp. 2810.
DOI Link 2306

Pique, G.[Gaétan], Carrer, D.[Dominique], Lugato, E.[Emanuele], Fieuzal, R.[Rémy], Garisoain, R.[Raphaël], Ceschia, E.[Eric],
About the Assessment of Cover Crop Albedo Potential Cooling Effect: Risk of the Darkening Feedback Loop Effects,
RS(15), No. 13, 2023, pp. 3231.
DOI Link 2307

Alagialoglou, L.[Leonidas], Manakos, I.[Ioannis], Papadopoulou, S.[Sofia], Chadoulis, R.T.[Rizos-Theodoros], Kita, A.[Afroditi],
Mapping Underwater Aquatic Vegetation Using Foundation Models With Air- and Space-Borne Images: The Case of Polyphytos Lake,
RS(15), No. 16, 2023, pp. 4001.
DOI Link 2309

Fan, X.S.[Xiang-Suo], Li, X.Y.[Xu-Yang], Yan, C.[Chuan], Fan, J.L.[Jin-Long], Chen, L.[Lin], Wang, N.[Nayi],
Converging Channel Attention Mechanisms with Multilayer Perceptron Parallel Networks for Land Cover Classification,
RS(15), No. 16, 2023, pp. 3924.
DOI Link 2309

Chandler, C.J.[Chris J.], Ávila-Mosqueda, S.V.[Silvia Valery], Salas-Acosta, E.R.[Evelyn Raquel], Magańa-Gallegos, E.[Eden], Mancera, E.E.[Edgar Escalante], Reali, M.A.G.[Miguel Angel Gómez], de la Barreda-Bautista, B.[Betsabé], Boyd, D.S.[Doreen S.], Metcalfe, S.E.[Sarah E.], Sjogersten, S.[Sofie], van Tussenbroek, B.[Brigitta], Silva, R.[Rodolfo], Foody, G.M.[Giles M.],
Spectral Characteristics of Beached Sargassum in Response to Drying and Decay over Time,
RS(15), No. 17, 2023, pp. 4336.
DOI Link 2310

Malambo, L.[Lonesome], Popescu, S.[Sorin],
Image to Image Deep Learning for Enhanced Vegetation Height Modeling in Texas,
RS(15), No. 22, 2023, pp. 5391.
DOI Link 2311

Wang, B.G.[Bao-Guo], Yao, Y.H.[Yong-Hui],
Mountain Vegetation Classification Method Based on Multi-Channel Semantic Segmentation Model,
RS(16), No. 2, 2024, pp. 256.
DOI Link 2402

Knox, D.[David], Xue, B.[Bing], Zhang, M.J.[Meng-Jie], Cuff, J.[Jeromy],
Measuring Ground Cover in Long Term Hill Country Photography using Weakly Supervised Convolutional Neural Networks,
Training, Photography, Analytical models, Annotations, Semantic segmentation, Supervised learning, Soil BibRef

Zheng, C.Y.[Chen-Yu], Wang, J.J.[Jun-Jue], Ma, A.[Ailong], Zhong, Y.F.[Yan-Fei],
AutoLC: Search Lightweight and Top-Performing Architecture for Remote Sensing Image Land-Cover Classification,
Training, Gradient methods, Image processing, Manuals, Search problems, Decoding BibRef

Aksoy, A.K.[Ahmet Kerem], Ravanbakhsh, M.[Mahdyar], Kreuziger, T.[Tristan], Demir, B.[Begüm],
A Consensual Collaborative Learning Method for Remote Sensing Image Classification Under Noisy Multi-Labels,
Collecting training samples. Training, Uncertainty, Image processing, Collaboration, Collaborative work, Sensors, Noise measurement, Multi-label noise, remote sensing BibRef

Ullah, H.[Habib], Ahmed, T.U.[Tawsin Uddin], Ullah, M.[Mohib], Cheikh, F.A.[Faouzi Alaya],
IR-SSL: Improved Regularization Based Semi-Supervised Learning for Land Cover Classification,
Satellites, Image processing, Urban planning, Estimation, Semisupervised learning, Benchmark testing, feature learning BibRef

Deneu, B.[Benjamin], Joly, A.[Alexis], Bonnet, P.[Pierre], Servajean, M.[Maximilien], Munoz, F.[François],
How Do Deep Convolutional SDM Trained on Satellite Images Unravel Vegetation Ecology?,
Springer DOI 2103

Zhang, Q., Zhang, Y., Yang, P., Meng, Y., Zhuo, S., Yang, Z.,
The Land Cover Classification Using A Feature Pyramid Networks Architecture From Satellite Imagery,
DOI Link 2012

Zhang, K., Yang, H.,
Semi-Supervised Multi-Spectral Land Cover Classification With Multi-Attention and Adaptive Kernel,
Feature extraction, Kernel, Remote sensing, Training, Generators, Convolution, Agriculture, Multi-Spectral, Multi-Attention BibRef

Baghbaderani, R.K.[Razieh Kaviani], Qu, Y.[Ying], Qi, H.R.[Hai-Rong], Stutts, C.[Craig],
Representative-Discriminative Learning for Open-Set Land Cover Classification of Satellite Imagery,
ECCV20(XXX: 1-17).
Springer DOI 2010

Rußwurm, M., Wang, S., Körner, M., Lobell, D.,
Meta-Learning for Few-Shot Land Cover Classification,
Task analysis, Adaptation models, Remote sensing, Data models, Image segmentation, Laser radar, Satellites BibRef

Artemeva, O.V., Zareie, S., Elhaei, Y., Pozdnyakova, N.A., Vasilev, N.D.,
Using Remote Sensing Data to Create Maps of Vegetation and Relief For Natural Resource Management of a Large Administrative Region,
DOI Link 1912

Abujayyab, S.K.M., Karas, I.R.,
Geospatial Machine Learning Datasets Structuring and Classification Tool: Case Study for Mapping LULC From Rasat Satellite Images,
DOI Link 1912

Yao, Y., Zhao, H., Huang, D., Tan, Q.,
Remote Sensing Scene Classification Using Multiple Pyramid Pooling,
DOI Link 1912

Rykin, I., Shagnieva, A., Panidi, E., Tsepelev, V.,
Highly Discrete Mapping of The Growing Season Time Frames and Time Dynamics,
DOI Link 1912
Based on vegetation index. BibRef

Requena-Mesa, C.[Christian], Reichstein, M.[Markus], Mahecha, M.D.[Miguel D.], Kraft, B.[Basil], Denzler, J.[Joachim],
Predicting Landscapes from Environmental Conditions Using Generative Networks,
Springer DOI 1911
More than land cover -- meaningful ecological units from images. BibRef

Rakhlin, A., Davydow, A., Nikolenko, S.,
Land Cover Classification from Satellite Imagery with U-Net and Lovász-Softmax Loss,
Image segmentation, Satellites, Training, Task analysis, Stochastic processes BibRef

Li, T., Comer, M., Zerubia, J.,
A Connected-Tube MPP Model for Object Detection with Application to Materials and Remotely-Sensed Images,
Shape, Mixed Marked Point Process, stochastic modeling, road and building detection BibRef

Wolfe, J., Jin, X., Bahr, T., Holzer, N.,
Application of Softmax Regression And Its Validation for Spectral-based Land Cover Mapping,
DOI Link 1805

Workman, S., Zhai, M., Crandall, D.J.[David J.], Jacobs, N.,
A Unified Model for Near and Remote Sensing,
feature extraction, feedforward neural nets, geophysical image processing, image resolution, land cover, Remote sensing BibRef

Khawaja, H.A.[Hassan A.],
Solution of Pure Scattering Radiation Transport Equation (RTE) Using Finite Difference Method (FDM),
SCIA17(I: 492-501).
Springer DOI 1706

Wirth, E., Szabó, G., Czinkóczky, A.,
Measure Landscape Diversity With Logical Scout Agents,
ISPRS16(B2: 491-495).
DOI Link 1610

Hosni, I., Bennaceur Farah, L., Naceur, M.S., Farah, I.R.,
Estimation Of Physical Parameters Of A Multilayered Multi-scale Vegetated Surface,
ISPRS16(B1: 453-460).
DOI Link 1610

Niederheiser, R.[Robert], Rutzinger, M.[Martin], Lamprecht, A.[Andrea], Steinbauer, K.[Klaus], Winkler, M.[Manuela], Pauli, H.[Harald],
Mapping Alpine Vegetation Location Properties By Dense Matching,
ISPRS16(B5: 881-886).
DOI Link 1610

Müllerová, J.[Jana], Bruna, J.[Josef], Dvorák, P.[Petr], Bartaloš, T.[Tomáš], Vítková, M.[Michaela],
Does The Data Resolution/origin Matter? Satellite, Airborne And UAV Imagery To Tackle Plant Invasions,
ISPRS16(B7: 903-908).
DOI Link 1610

Aswatha, S.M., Mukhopadhyay, J., Biswas, P.K.,
Spectral slopes for automated classification of land cover in landsat images,
Earth BibRef

Jay, S., Bendoula, R., Hadoux, X., Gorretta, N.,
Mapping of Foliar Content Using Radiative Transfer Modeling and VIS-NIR Hyperspectral Close-Range Remote-Sensing,
DOI Link 1602
Details based on leaf properties. BibRef

Santana, T.M.H.C.[Tiago M.H.C.], Machado, A.M.C.[Alexei M.C.], de Albuquerque Araújo, A.[Arnaldo], dos Santos, J.A.[Jefersson A.],
Star: A Contextual Description of Superpixels for Remote Sensing Image Classification,
Springer DOI 1703

de Andrade, Jr., E.F.[Edemir Ferreira], de Albuquerque Araújo, A.[Arnaldo], dos Santos, J.A.[Jefersson A.],
A Multiclass Approach for Land-Cover Mapping by Using Multiple Data Sensors,
Springer DOI 1511

Dhawale, N.M., Adamchuk, V.I., Prasher, S.O., Dutilleul, P.R.L., Ferguson, R.B.,
Spatially Constrained Geospatial Data Clustering for Multilayer Sensor-Based Measurements,
DOI Link 1411

Ustuner, M., Sanli, F.B., Abdikan, S., Esetlili, M.T., Kurucu, Y.,
Crop Type Classification Using Vegetation Indices of RapidEye Imagery,
DOI Link 1404

Brocks, S., Bareth, G.,
Evaluating the potential of consumer-grade smart cameras for low-cost stereo-photogrammetric Crop-Surface Monitoring,
DOI Link 1404

Jia, Y., Li, H.T., Gu, H.Y., Han, Y.S.,
Study on the Technology and Method of Land Cover Classification for Geographic National Conditions Surveying,
DOI Link 1311

Hu, B., Li, P.,
A Comparative Study Between Pair-Point Clique and Multi-Point Clique Markov Random Field Models for Land Cover Classification,
DOI Link 1311

Bradbury, G.[Gwyneth], Mitchell, K.[Kenny], Weyrich, T.[Tim],
Multi-spectral Material Classification in Landscape Scenes Using Commodity Hardware,
Springer DOI 1311

Moody, D.I., Bauer, D.E., Brumby, S.P., Chisolm, E.D., Warren, M.S., Skillman, S.W., Keisler, R.,
Land cover classification in fused multisensor multispectral satellite imagery,
Earth BibRef

Moody, D.I., Brumby, S.P., Rowland, J.C., Altmann, G.L., Larson, A.E.,
Change detection and classification of land cover in multispectral satellite imagery using clustering of sparse approximations (CoSA) over learned feature dictionaries,
Hebbian learning BibRef

Warren, M.S., Brumby, S.P., Skillman, S.W., Kelton, T., Wohlberg, B., Mathis, M., Chartrand, R., Keisler, R., Johnson, M.,
Seeing the Earth in the Cloud: Processing one petabyte of satellite imagery in one day,
cloud computing BibRef

Moody, D.I., Wozniak, P.R., Brumby, S.P.,
Automated variability selection in time-domain imaging surveys using sparse representations with learned dictionaries,
computer vision BibRef

Moody, D.I., Brumby, S.P., Rowland, J.C., Gangodagamage, C.,
Unsupervised land cover classification in multispectral imagery with sparse representations on learned dictionaries,
Hebbian learning BibRef

Negri, R.G.[Rogério G.], Casaca, W.C.O.[Wallace C. O.], Silva, E.A.[Erivaldo A.],
Region-Based Classification of PolSAR Data Through Kernel Methods and Stochastic Distances,
Springer DOI 1802

Negri, R.G.[Rogério G.], Dutra, L.V.[Luciano V.], Sant'Anna, S.J.S.[Sidnei J.S.],
Stochastic Approaches of Minimum Distance Method for Region Based Classification,
Springer DOI 1209

Niroumand Jadidi, M., Safdarinezhad, A.R., Sahebi, M.R., Mokhtarzade, M.,
A Novel Approach To Super Resolution Mapping of Multispectral Imagery Based On Pixel Swapping Technique,
AnnalsPRS(I-7), No. 2012, pp. 159-164.
DOI Link 1209

Mccamley, G., Grant, I., Jones, S., Bellman, C.,
Characterising Vegetated Surfaces Using Modis Multiangular Satellite Data,
AnnalsPRS(I-7), No. 2012, pp. 13-18.
DOI Link 1209

Handique, B.K.,
A Class of Regression-cum-ratio Estimators In Two-phase Sampling For Utilizing Information From High Resolution Satellite Data,
AnnalsPRS(I-4), No. 2012, pp. 71-76.
DOI Link 1209

Büschenfeld, T., Ostermann, J.,
Automatic Refinement of Training Data for Classification of Satellite Imagery,
AnnalsPRS(I-7), No. 2012, pp. 117-122.
DOI Link 1209

Hosomura, T.,
Improvement Of Thermal Estimation At Land Cover Boundary By Using Quantile,
DOI Link 1209

Zhai, L., Sun, J., Sang, H., Yang, G., Jia, Y.,
Large Area Land Cover Classification With Landsat Etm+ Images Based On Decision Tree,
DOI Link 1209

Nilsen, A.B., Bjřrkelo, K.,
National Land Cover And Resource Statistics,
DOI Link 1209

Fadaei, H., Suzuki, R., Sakai, T., Torii, K.,
A Proposed New Vegetation Index, The Total Ratio Vegetation Index (trvi), For Arid And Semi-arid Regions,
DOI Link 1209

Bareth, G., Waldhoff, G.,
Regionalization of Agricultural Management by Using the Multi-Data Approach (MDA),
DOI Link 1209

Petrou, Z.I., Tarantino, C., Adamo, M., londa, P., Petrou, M.,
Estimation Of Vegetation Height Through Satellite Image Texture Analysis,
DOI Link 1209

Peterman, V., Mesaric(, M.,
Land Survey From Unmaned Aerial Veichle,
DOI Link 1209

Li, W.W.[Wei-Wei], Du, J.[Jian], Yi, B.L.[Bao-Lin],
Study on classification for vegetation spectral feature extraction method based on decision tree algorithm,

Sarhan, E.[Ebada], Khalifa, E.[Eraky], Nabil, A.M.[Ayman M.],
Post classification using Cellular Automata for Landsat images in developing countries,

Fang, Y.M.[Yuan-Min], Chen, J.[Jie], Xia, Y.H.[Yong-Hua], Song, W.W.[Wei-Wei], Yang, Y.M.[Yong-Ming],
Research on Adaptive Classification Algorithm of Remote Sensing Image,

Satalino, G.[Giuseppe], Impedovo, D.[Donato], Balenzano, A.[Anna], Mattia, F.[Francesco],
Land cover classification by using multi-temporal COSMO-SkyMed data,

Zabala, A., Cea, C., Pons, X.,
Segmentation and Thematic Classification of Color Orthophotos Over Non-Compressed And JPEG 2000 Compressed Images,
PDF File. 1007

Mancini, A.[Adriano], Tassetti, A.N.[Anna Nora],
A Novel Method for Fast Processing of Large Remote Sensed Image,
Springer DOI 1309

Gomah, M.[Mahmoud], Trinder, J.[John], Shaker, A.[Ahmed], Hamed, M.[Mahmoud], Elsagheer, A.[Ali],
Integrating multiple classifiers with fuzzy majority voting for improved land cover classification,
PDF File. 1009

Xiong, B.[Biao], Zhang, X.J.[Xiao-Jun], Jiang, W.[Wanshou],
Semi-supervised Classification Based On Gauss Mixture Model For Remote Imagery,
VCGVA09(xx-yy). 0910
Virtual Globe; Remote Sensing Image; Thematic Information; Semi-Supervised Classification; Gauss Mixture Model; EM algorithms BibRef

Zuo, L.J.[Li-Jun], Dong, T.T.[Ting-Ting], Wang, X.[Xiao], Zhao, X.L.[Xiao-Li], Yi, L.[Ling], Liu, B.[Bin],
A new method of MCI extraction with multi-temporal MODIS EVI data,
multiple cropping index. BibRef

Morimoto, T.[Tetsuro], Ikeuchi, K.[Katsushi],
Multispectral imaging for material analysis in an outdoor environment using Normalized Cuts,
Not really crops, but similar result. BibRef

Fang, L.[Lei], Jiang, T.[Tao], Shan, C.Z.[Chun-Zhi], Li, H.W.[Hai-Wei],
A Per-Pixel Stratified Classification Methodology for Land Cover Mapping Based on Medium-Resolution Satellite Imagery,

Qian, Y.R.[Yu Rong], Yang, F.[Feng], Li, J.L.[Jian Long],
Wavelet-Based Feature Extraction for Retrieval of Photosynthetic Pigment Concentrations from Hyperspectral Reflectance,

Zingaretti, P.[Primo], Frontoni, E.[Emanuele], Malinverni, E.S.[Eva Savina], Mancini, A.[Adriano],
A Hybrid Approach to Land Cover Classification from Multi Spectral Images,
Springer DOI 0909

Wang, O.[Oliver], Gunawardane, P.[Prabath], Scher, S.[Steve], Davis, J.[James],
Material classification using BRDF slices,
Bidirectional Reflectance Distribution Function BibRef

López, A.A.[Adrian A.], Malpica, J.A.[José A.],
High Resolution Satellite Classification with Graph Cut Algorithms,
ISVC08(II: 105-112).
Springer DOI 0812

Alonso, M.C.[María C.], Sanz, M.A.[María A.], Malpica, J.A.[José A.],
Classification of High Resolution Satellite Images Using Texture from the Panchromatic Band,
ISVC07(II: 499-508).
Springer DOI 0711

Shkvarko, Y.V.[Yuriy V.], Villalon-Turrubiates, I.E.[Ivan E.],
Remote Sensing Imagery and Signature Fields Reconstruction Via Aggregation of Robust Regularization with Neural Computing,
Springer DOI 0708

Ferreiro-Armán, M.[Marcos], Bandeira, L.P.C.[Lourenço P. C.], Martín-Herrero, J.[Julio], Pina, P.[Pedro],
Classifiers for Vegetation and Forest Mapping with Low Resolution Multiespectral Imagery,
IbPRIA07(I: 177-184).
Springer DOI 0706

Yang, Y.F.[Yeh Fen], Lohmann, P.[Peter], Heipke, C.[Christian],
Genetic Algorithms for the Unsupervised Classification of Satellite Images,
PDF File. 0609

Ohkubo, A.[Akito], Niijima, K.[Koichi],
New Supervised Learning of Neural Networks for satellite image classification,
IEEE DOI Land Cover Classification BibRef 9900

Chapter on Remote Sensing General Issue, Land Use, Land Cover continues in
Land Use, General Problems .

Last update:Apr 18, 2024 at 11:38:49