22.1.6 Remote Sensing Issues, Evaluations of Techniques, Validation

Chapter Contents (Back)
Remote Sensing. Evaluation, Remote Sensing.

Defries, R.S., Chan, J.C.W.[Jonathan Cheung-Wai],
Multiple Criteria for Evaluating Machine Learning Algorithms for Land Cover Classification from Satellite Data,
RSE(74), No. 3, 2000, pp. 503-515. 0102

Özkan, C.[Coskun], Erbek, F.S.[Filiz Sunar],
A Comparison of Activation Functions for Multispectral Landsat TM Image Classification,
PhEngRS(69), No. 11, November 2003, pp. 1225-1234.
WWW Link. 0401
Compare linear, sigmoid, and tangent hyperbolic activation functions through the one- and two-hidden layered MLP neural network structures trained with the scaled conjugate gradient learning algorithm, and evaluate their perfornances for a multispectral Landsat TM imagery hard classification problem. BibRef

Makido, Y.[Yasuyo], Shortridge, A.[Ashton], Messina, J.P.[Joseph P.],
Assessing Alternatives for Modeling the Spatial Distribution of Multiple Land-cover Classes at Sub-pixel Scales,
PhEngRS(73), No. 8, August 2007, pp. 935-944.
WWW Link. 0709
Evaluating three methods for modeling the spatial distribution of multiple land cover classes at sub-pixel scales. BibRef

Yang, P., Shibasaki, R., Wu, W., Zhou, Q., Chen, Z., Zha, Y., Shi, Y., Tang, H.,
Evaluation of MODIS Land Cover and LAI Products in Cropland of North China Plain Using In Situ Measurements and Landsat TM Images,
GeoRS(45), No. 10, October 2007, pp. 3087-3097.

Chen, D.M.[Dong-Mei],
A Standardized Probability Comparison Approach for Evaluating and Combining Pixel-based Classification Procedures,
PhEngRS(74), No. 5, May 2008, pp. 601-610.
WWW Link. 0804
An objective approach to evaluate pixel labeling confidence in a classification and to combine classified maps generated from different classification procedures. BibRef

Aitkenhead, M.J., Flaherty, S., Cutler, M.E.J.,
Evaluating Neural Networks and Evidence Pooling for Land Cover Mapping,
PhEngRS(74), No. 8, August 2008, pp. 1019-1032.
WWW Link. 0804
Integrating evidence from a range of data sources was to produce land cover mapping based on neural networks trained to identify specific land cover classes. BibRef

Lowry, Jr., J.H.[John H.], Ramsey, R.D.[R. Douglas], Stoner, L.L.[Lisa Langs], Kirby, J.[Jessica], Schulz, K.[Keith],
An Ecological Framework for Evaluating Map Errors Using Fuzzy Sets,
PhEngRS(74), No. 12, December 2008, pp. 1509-1520.
WWW Link. 0804
Using an ecological context to define varying levels of landcover class similarity, a decision framework guides map experts' decisions and provides a more meaningful assessment of map errors using fuzzy sets. BibRef

Balaguer, A., Ruiz, L.A., Hermosilla, T., Recio, J.A.,
Definition of a comprehensive set of texture semivariogram features and their evaluation for object-oriented image classification,
CompGeoSci(36), No. 2, February 2010, pp. 231-240.
Elsevier DOI Remote sensing, Feature extraction, Texture descriptors, Image classification 1204

Balaguer-Besser, A., Hermosilla, T., Recio, J.A., Ruiz, L.A.,
Semivariogram calculation optimization for object-oriented image classification,
Other JournalModelling in Science Education and Learning(4), No. 7, 2011, pp. 91-104.
PDF File. 1204

Murray-Tortarolo, G.[Guillermo], Anav, A.[Alessandro], Friedlingstein, P.[Pierre], Sitch, S.[Stephen], Piao, S.L.[Shi-Long], Zhu, Z.C.[Zai-Chun], Poulter, B.[Benjamin], Zaehle, S.[Soenke], Ahlström, A.[Anders], Lomas, M.[Mark], Levis, S.[Sam], Viovy, N.[Nicholas], Zeng, N.[Ning],
Evaluation of Land Surface Models in Reproducing Satellite-Derived LAI over the High-Latitude Northern Hemisphere. Part I: Uncoupled DGVMs,
RS(5), No. 10, 2013, pp. 4819-4838.
DOI Link 1311
And: A2, A1, A3, A4, A5, A6, Only:
Evaluation of Land Surface Models in Reproducing Satellite Derived Leaf Area Index over the High-Latitude Northern Hemisphere. Part II: Earth System Models,
RS(5), No. 8, 2013, pp. 3637-3661.
DOI Link 1309

Ahmed, B.[Bayes], Ahmed, R.[Raquib], Zhu, X.[Xuan],
Evaluation of Model Validation Techniques in Land Cover Dynamics,
IJGI(2), No. 3, 2013, pp. 577-597.
DOI Link 1307

Chen, J.[Jing], Zhang, H.F.[Hui-Fang], Liu, Z.R.[Zi-Rui], Che, M.L.[Ming-Liang], Chen, B.Z.[Bao-Zhang],
Evaluating Parameter Adjustment in the MODIS Gross Primary Production Algorithm Based on Eddy Covariance Tower Measurements,
RS(6), No. 4, 2014, pp. 3321-3348.
DOI Link 1405

Löw, F.[Fabian], Duveiller, G.[Grégory],
Defining the Spatial Resolution Requirements for Crop Identification Using Optical Remote Sensing,
RS(6), No. 9, 2014, pp. 9034-9063.
DOI Link 1410

Glanz, H.[Hunter], Carvalho, L.[Luis], Sulla-Menashe, D.[Damien], Friedl, M.A.[Mark A.],
A parametric model for classifying land cover and evaluating training data based on multi-temporal remote sensing data,
PandRS(97), No. 1, 2014, pp. 219-228.
Elsevier DOI 1410
Maximum likelihood estimation BibRef

Mellor, A.[Andrew], Boukir, S.[Samia], Haywood, A.[Andrew], Jones, S.[Simon],
Exploring Issues of Training Data Imbalance and Mislabelling on Random Forest Performance for Large Area Land Cover Classification Using the Ensemble Margin,
PandRS(105), No. 1, 2015, pp. 155-168.
Elsevier DOI 1506
Using ensemble margin to explore issues of training data imbalance and mislabeling on large area land cover classification,
Ensemble margin Accuracy See also Fast Data Selection for SVM Training Using Ensemble Margin. BibRef

Mellor, A.[Andrew], Boukir, S.[Samia],
Exploring diversity in ensemble classification: Applications in large area land cover mapping,
PandRS(129), No. 1, 2017, pp. 151-161.
Elsevier DOI 1706
Diversity BibRef

Piles, M., McColl, K.A., Entekhabi, D., Das, N., Pablos, M.,
Sensitivity of Aquarius Active and Passive Measurements Temporal Covariability to Land Surface Characteristics,
GeoRS(53), No. 8, August 2015, pp. 4700-4711.
Land surface BibRef

Shi, W.Z.[Wen-Zhong], Zhang, X.K.[Xiao-Kang], Hao, M.[Ming], Shao, P.[Pan], Cai, L.P.[Li-Ping], Lyu, X.[Xuzhe],
Validation of Land Cover Products Using Reliability Evaluation Methods,
RS(7), No. 6, 2015, pp. 7846.
DOI Link 1507

Guanter, L.[Luis], Kaufmann, H.[Hermann], Segl, K.[Karl], Foerster, S.[Saskia], Rogass, C.[Christian], Chabrillat, S.[Sabine], Kuester, T.[Theres], Hollstein, A.[André], Rossner, G.[Godela], Chlebek, C.[Christian], Straif, C.[Christoph], Fischer, S.[Sebastian], Schrader, S.[Stefanie], Storch, T.[Tobias], Heiden, U.[Uta], Mueller, A.[Andreas], Bachmann, M.[Martin], Mühle, H.[Helmut], Müller, R.[Rupert], Habermeyer, M.[Martin], Ohndorf, A.[Andreas], Hill, J.[Joachim], Buddenbaum, H.[Henning], Hostert, P.[Patrick], van der Linden, S.[Sebastian], Leităo, P.J.[Pedro J.], Rabe, A.[Andreas], Doerffer, R.[Roland], Krasemann, H.[Hajo], Xi, H.Y.[Hong-Yan], Mauser, W.[Wolfram], Hank, T.[Tobias], Locherer, M.[Matthias], Rast, M.[Michael], Staenz, K.[Karl], Sang, B.[Bernhard],
The EnMAP Spaceborne Imaging Spectroscopy Mission for Earth Observation,
RS(7), No. 7, 2015, pp. 8830.
DOI Link 1506
Award, Remote Sensing 10th, Rank 3. BibRef

Sun, L.[Liya], Schulz, K.[Karsten],
The Improvement of Land Cover Classification by Thermal Remote Sensing,
RS(7), No. 7, 2015, pp. 8368-8390.
DOI Link 1506
And: Response to comments: RS(7), No. 10, 2015, pp. 13440.
DOI Link 1511
See also Scale Issues Related to the Accuracy Assessment of Land Use/Land Cover Maps Produced Using Multi-Resolution Data: Comments on 'The Improvement of Land Cover Classification by Thermal Remote Sensing'. See also Automatic Procedure for Early Disaster Change Mapping Based on Optical Remote Sensing, An. See also We Must all Pay More Attention to Rigor in Accuracy Assessment: Additional Comment to The Improvement of Land Cover Classification by Thermal Remote Sensing. Remote Sens. 2015, 7, 8368-8390. BibRef

Johnson, B.A.[Brian A.],
Scale Issues Related to the Accuracy Assessment of Land Use/Land Cover Maps Produced Using Multi-Resolution Data: Comments on 'The Improvement of Land Cover Classification by Thermal Remote Sensing',
RS(7), No. 10, 2015, pp. 13436.
DOI Link 1511
Original paper and response. See also Improvement of Land Cover Classification by Thermal Remote Sensing, The. BibRef

Ma, Y.[Yong], Chen, F.[Fu], Liu, J.B.[Jian-Bo], He, Y.[Yang], Duan, J.B.[Jian-Bo], Li, X.[Xinpeng],
An Automatic Procedure for Early Disaster Change Mapping Based on Optical Remote Sensing,
RS(8), No. 4, 2016, pp. 272.
DOI Link 1604
See also Improvement of Land Cover Classification by Thermal Remote Sensing, The. BibRef

Castilla, G.[Guillermo],
We Must all Pay More Attention to Rigor in Accuracy Assessment: Additional Comment to 'The Improvement of Land Cover Classification by Thermal Remote Sensing'. Remote Sens. 2015, 7, 8368-8390,
RS(8), No. 4, 2016, pp. 288.
DOI Link 1604
See also Improvement of Land Cover Classification by Thermal Remote Sensing, The. BibRef

Aasen, H.[Helge], Burkart, A.[Andreas], Bolten, A.[Andreas], Bareth, G.[Georg],
Generating 3D hyperspectral information with lightweight UAV snapshot cameras for vegetation monitoring: From camera calibration to quality assurance,
PandRS(108), No. 1, 2015, pp. 245-259.
Elsevier DOI 1511
Hyperspectral digital surface model BibRef

Aasen, H.[Helge], Bendig, J., Bolten, A.[Andreas], Bennertz, S., Willkomm, M., Bareth, G.[Georg],
Introduction and preliminary results of a calibration for full-frame hyperspectral cameras to monitor agricultural crops with UAVs,
DOI Link 1404

Mesas-Carrascosa, F.J.[Francisco-Javier], Torres-Sánchez, J.[Jorge], Clavero-Rumbao, I.[Inmaculada], García-Ferrer, A.[Alfonso], Peńa, J.M.[Jose-Manuel], Borra-Serrano, I.[Irene], López-Granados, F.[Francisca],
Assessing Optimal Flight Parameters for Generating Accurate Multispectral Orthomosaicks by UAV to Support Site-Specific Crop Management,
RS(7), No. 10, 2015, pp. 12793.
DOI Link 1511

Verrelst, J.[Jochem], Camps-Valls, G.[Gustau], Muńoz-Marí, J.[Jordi], Rivera, J.P.[Juan Pablo], Veroustraete, F.[Frank], Clevers, J.G.P.W.[Jan G.P.W.], Moreno, J.[José],
Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties: A review,
PandRS(108), No. 1, 2015, pp. 273-290.
Elsevier DOI 1511
Bio-geophysical variables BibRef

She, X.J.[Xiao-Jun], Zhang, L.[Lifu], Cen, Y.[Yi], Wu, T.[Taixia], Huang, C.P.[Chang-Ping], Baig, M.H.A.[Muhammad Hasan Ali],
Comparison of the Continuity of Vegetation Indices Derived from Landsat 8 OLI and Landsat 7 ETM+ Data among Different Vegetation Types,
RS(7), No. 10, 2015, pp. 13485.
DOI Link 1511

Abade, N.A.[Natanael Antunes], de Carvalho Júnior, O.A.[Osmar Abílio], Guimarăes, R.F.[Renato Fontes], de Oliveira, S.N.[Sandro Nunes],
Comparative Analysis of MODIS Time-Series Classification Using Support Vector Machines and Methods Based upon Distance and Similarity Measures in the Brazilian Cerrado-Caatinga Boundary,
RS(7), No. 9, 2015, pp. 12160.
DOI Link 1511

Bontemps, S.[Sophie], Arias, M.[Marcela], Cara, C.[Cosmin], Dedieu, G.[Gérard], Guzzonato, E.[Eric], Hagolle, O.[Olivier], Inglada, J.[Jordi], Matton, N.[Nicolas], Morin, D.[David], Popescu, R.[Ramona], Rabaute, T.[Thierry], Savinaud, M.[Mickael], Sepulcre, G.[Guadalupe], Valero, S.[Silvia], Ahmad, I.[Ijaz], Bégué, A.[Agnčs], Wu, B.[Bingfang], de Abelleyra, D.[Diego], Diarra, A.[Alhousseine], Dupuy, S.[Stéphane], French, A.[Andrew], ul Hassan Akhtar, I.[Ibrar], Kussul, N.[Nataliia], Lebourgeois, V.[Valentine], Page, M.L.[Michel Le], Newby, T.[Terrence], Savin, I.[Igor], Verón, S.R.[Santiago R.], Koetz, B.[Benjamin], Defourny, P.[Pierre],
Building a Data Set over 12 Globally Distributed Sites to Support the Development of Agriculture Monitoring Applications with Sentinel-2,
RS(7), No. 12, 2015, pp. 15815.
DOI Link 1601

Costa, H.[Hugo], Foody, G.M.[Giles M.], Jiménez, S.[Sílvia], Silva, L.[Luís],
Impacts of Species Misidentification on Species Distribution Modeling with Presence-Only Data,
IJGI(4), No. 4, 2015, pp. 2496.
DOI Link 1601

Griffith, D.A.[Daniel A.], Chun, Y.[Yongwan],
Spatial Autocorrelation and Uncertainty Associated with Remotely-Sensed Data,
RS(8), No. 7, 2016, pp. 535.
DOI Link 1608

Schima, R.[Robert], Mollenhauer, H.[Hannes], Grenzdörffer, G.J.[Görres J.], Merbach, I.[Ines], Lausch, A.[Angela], Dietrich, P.[Peter], Bumberger, J.[Jan],
Imagine All the Plants: Evaluation of a Light-Field Camera for On-Site Crop Growth Monitoring,
RS(8), No. 10, 2016, pp. 823.
DOI Link 1609

Yang, Y.[Yongke], Xiao, P.F.[Peng-Feng], Feng, X.Z.[Xue-Zhi], Li, H.X.[Hai-Xing],
Accuracy assessment of seven global land cover datasets over China,
PandRS(125), No. 1, 2017, pp. 156-173.
Elsevier DOI 1703
Global land cover dataset BibRef

Xia, G.S., Hu, J., Hu, F., Shi, B., Bai, X., Zhong, Y., Zhang, L., Lu, X.,
AID: A Benchmark Data Set for Performance Evaluation of Aerial Scene Classification,
GeoRS(55), No. 7, July 2017, pp. 3965-3981.
Benchmark testing, Earth, Google, Performance evaluation, Remote sensing, Rivers, Semantics, Aerial images, benchmark, scene, classification BibRef

Kharbouche, S.[Said], Muller, J.P.[Jan-Peter], Gatebe, C.K.[Charles K.], Scanlon, T.[Tracy], Banks, A.C.[Andrew C.],
Assessment of Satellite-Derived Surface Reflectances by NASA's CAR Airborne Radiometer over Railroad Valley Playa,
RS(9), No. 6, 2017, pp. xx-yy.
DOI Link 1706

Radoux, J.[Julien], Bogaert, P.[Patrick],
Good Practices for Object-Based Accuracy Assessment,
RS(9), No. 7, 2017, pp. xx-yy.
DOI Link 1708

Cheng, G., Han, J., Lu, X.,
Remote Sensing Image Scene Classification: Benchmark and State of the Art,
PIEEE(105), No. 10, October 2017, pp. 1865-1883.
data sets, data-driven algorithms, image diversity, image numbers, image variations, learning based methods, BibRef

Phiri, D.[Darius], Morgenroth, J.[Justin],
Developments in Landsat Land Cover Classification Methods: A Review,
RS(9), No. 9, 2017, pp. xx-yy.
DOI Link 1711

Sun, P.J.[Pei-Jun], Congalton, R.G.[Russell G.], Grybas, H.[Heather], Pan, Y.Z.[Yao-Zhong],
The Impact of Mapping Error on the Performance of Upscaling Agricultural Maps,
RS(9), No. 9, 2017, pp. xx-yy.
DOI Link 1711

Li, J.[Jian], Roy, D.P.[David P.],
A Global Analysis of Sentinel-2A, Sentinel-2B and Landsat-8 Data Revisit Intervals and Implications for Terrestrial Monitoring,
RS(9), No. 9, 2017, pp. xx-yy.
DOI Link 1711

Isidro, C.M.[Celso M.], McIntyre, N.[Neil], Lechner, A.M.[Alex M.], Callow, I.[Ian],
Applicability of Earth Observation for Identifying Small-Scale Mining Footprints in a Wet Tropical Region,
RS(9), No. 9, 2017, pp. xx-yy.
DOI Link 1711

Zou, X.C.[Xiao-Chen], Mőttus, M.[Matti],
Sensitivity of Common Vegetation Indices to the Canopy Structure of Field Crops,
RS(9), No. 10, 2017, pp. xx-yy.
DOI Link 1711

Wang, S.H.[Si-Heng], Yang, D.[Dong], Li, Z.[Zhen], Liu, L.Y.[Liang-Yun], Huang, C.P.[Chang-Ping], Zhang, L.[Lifu],
A Global Sensitivity Analysis of Commonly Used Satellite-Derived Vegetation Indices for Homogeneous Canopies Based on Model Simulation and Random Forest Learning,
RS(11), No. 21, 2019, pp. xx-yy.
DOI Link 1911

Gao, J.[Jing], Burt, J.E.[James E.],
Per-pixel bias-variance decomposition of continuous errors in data-driven geospatial modeling: A case study in environmental remote sensing,
PandRS(134), No. Supplement C, 2017, pp. 110-121.
Elsevier DOI 1712
Model evaluation, Accuracy assessment, Bias-variance decomposition, Absolute error, Squared error BibRef

Rajbhandari, S.[Sachit], Aryal, J.[Jagannath], Osborn, J.[Jon], Musk, R.[Rob], Lucieer, A.[Arko],
Benchmarking the Applicability of Ontology in Geographic Object-Based Image Analysis,
IJGI(6), No. 12, 2017, pp. xx-yy.
DOI Link 1801

Gallo, K.[Kevin], Stensaas, G.[Greg], Dwyer, J.[John], Longhenry, R.[Ryan],
A Land Product Characterization System for Comparative Analysis of Satellite Data and Products,
RS(10), No. 1, 2018, pp. xx-yy.
DOI Link 1802

Berger, K.[Katja], Atzberger, C.[Clement], Danner, M.[Martin], d'Urso, G.[Guido], Mauser, W.[Wolfram], Vuolo, F.[Francesco], Hank, T.[Tobias],
Evaluation of the PROSAIL Model Capabilities for Future Hyperspectral Model Environments: A Review Study,
RS(10), No. 1, 2018, pp. xx-yy.
DOI Link 1802

Goldblatt, R.[Ran], Ballesteros, A.R.[Alexis Rivera], Burney, J.[Jennifer],
High Spatial Resolution Visual Band Imagery Outperforms Medium Resolution Spectral Imagery for Ecosystem Assessment in the Semi-Arid Brazilian Sertăo,
RS(9), No. 12, 2017, pp. xx-yy.
DOI Link 1802

Salk, C.[Carl], Fritz, S.[Steffen], See, L.[Linda], Dresel, C.[Christopher], McCallum, I.[Ian],
An Exploration of Some Pitfalls of Thematic Map Assessment Using the New Map Tools Resource,
RS(10), No. 3, 2018, pp. xx-yy.
DOI Link 1804

Ye, S.[Su], Pontius, R.G.[Robert Gilmore], Rakshit, R.[Rahul],
A review of accuracy assessment for object-based image analysis: From per-pixel to per-polygon approaches,
PandRS(141), 2018, pp. 137-147.
Elsevier DOI 1806
Accuracy assessment, Object-based image analysis, OBIA, Remote sensing, Per-pixel, Per-polygon BibRef

Sukhova, E.[Ekaterina], Sukhov, V.[Vladimir],
Connection of the Photochemical Reflectance Index (PRI) with the Photosystem II Quantum Yield and Nonphotochemical Quenching Can Be Dependent on Variations of Photosynthetic Parameters among Investigated Plants: A Meta-Analysis,
RS(10), No. 5, 2018, pp. xx-yy.
DOI Link 1806

Mőisja, K.[Kiira], Uuemaa, E.[Evelyn], Oja, T.[Tőnu],
The Implications of Field Worker Characteristics and Landscape Heterogeneity for Classification Correctness and the Completeness of Topographical Mapping,
IJGI(7), No. 6, 2018, pp. xx-yy.
DOI Link 1806

Colin, B.[Brigitte], Schmidt, M.[Michael], Clifford, S.[Samuel], Woodley, A.[Alan], Mengersen, K.[Kerrie],
Influence of Spatial Aggregation on Prediction Accuracy of Green Vegetation Using Boosted Regression Trees,
RS(10), No. 8, 2018, pp. xx-yy.
DOI Link 1809

Deng, L.[Lei], Yan, Y.[Yanan], Gong, H.L.[Hui-Li], Duan, F.Z.[Fu-Zhou], Zhong, R.F.[Ruo-Fei],
The effect of spatial resolution on radiometric and geometric performances of a UAV-mounted hyperspectral 2D imager,
PandRS(144), 2018, pp. 298-314.
Elsevier DOI 1809
Hyperspectral imaging, High spatial resolution, Unmanned aerial vehicles (UAVs), Radiometry, Geometric performance BibRef

Verde, N.[Natalia], Mallinis, G.[Giorgos], Tsakiri-Strati, M.[Maria], Georgiadis, C.[Charalampos], Patias, P.[Petros],
Assessment of Radiometric Resolution Impact on Remote Sensing Data Classification Accuracy,
RS(10), No. 8, 2018, pp. xx-yy.
DOI Link 1809
Higher resolution not necessarily reduces errors in analysis. BibRef

Kumar, L.[Lalit], Mutanga, O.[Onisimo],
Google Earth Engine Applications Since Inception: Usage, Trends, and Potential,
RS(10), No. 10, 2018, pp. xx-yy.
DOI Link 1811

Zhang, J.X.[Jing-Xiong], Yang, W.J.[Wen-Jing], Zhang, W.[Wangle], Wang, Y.[Yu], Liu, D.[Di], Xiu, Y.C.[Ying-Chang],
An Explorative Study on Estimating Local Accuracies in Land-Cover Information Using Logistic Regression and Class-Heterogeneity-Stratified Data,
RS(10), No. 10, 2018, pp. xx-yy.
DOI Link 1811

Ernst, S.[Stefan], Lymburner, L.[Leo], Sixsmith, J.[Josh],
Implications of Pixel Quality Flags on the Observation Density of a Continental Landsat Archive,
RS(10), No. 10, 2018, pp. xx-yy.
DOI Link 1811

Sertel, E.[Elif], Topaloglu, R.H.[Raziye Hale], Salli, B.[Betül], Algan, I.Y.[Irmak Yay], Aksu, G.A.[Gül Asli],
Comparison of Landscape Metrics for Three Different Level Land Cover/Land Use Maps,
IJGI(7), No. 10, 2018, pp. xx-yy.
DOI Link 1811

Stachon, Z.[Zdenek], Šašinka, C.[Cenek], Cenek, J.[Jirí], Angsüsser, S.[Stephan], Kubícek, P.[Petr], Šterba, Z.[Zbynek], Bilíková, M.[Martina],
Effect of Size, Shape and Map Background in Cartographic Visualization: Experimental Study on Czech and Chinese Populations,
IJGI(7), No. 11, 2018, pp. xx-yy.
DOI Link 1812

Burrell, A.L.[Arden L.], Evans, J.P.[Jason P.], Liu, Y.[Yi],
The impact of dataset selection on land degradation assessment,
PandRS(146), 2018, pp. 22-37.
Elsevier DOI 1812
RESTREND, BFAST, Dryland degradation, NDVI, Trend analysis, AVHRR, GIMMS, TSS-RESTREND BibRef

Jian, L.[Ling], Gao, F.[Fuhao], Ren, P.[Peng], Song, Y.Q.[Yun-Quan], Luo, S.[Shihua],
A Noise-Resilient Online Learning Algorithm for Scene Classification,
RS(10), No. 11, 2018, pp. xx-yy.
DOI Link 1812

Hua, T.[Ting], Zhao, W.[Wenwu], Liu, Y.X.[Yan-Xu], Wang, S.[Shuai], Yang, S.[Siqi],
Spatial Consistency Assessments for Global Land-Cover Datasets: A Comparison among GLC2000, CCI LC, MCD12, GLOBCOVER and GLCNMO,
RS(10), No. 11, 2018, pp. xx-yy.
DOI Link 1812

Carranza-García, M.[Manuel], García-Gutiérrez, J.[Jorge], Riquelme, J.C.[José C.],
A Framework for Evaluating Land Use and Land Cover Classification Using Convolutional Neural Networks,
RS(11), No. 3, 2019, pp. xx-yy.
DOI Link 1902

Ma, L.[Lei], Liu, Y.[Yu], Zhang, X.L.[Xue-Liang], Ye, Y.X.[Yuan-Xin], Yin, G.[Gaofei], Johnson, B.A.[Brian Alan],
Deep learning in remote sensing applications: A meta-analysis and review,
PandRS(152), 2019, pp. 166-177.
Elsevier DOI 1905
Deep learning (DL), Remote sensing, LULC classification, Object detection, Scene classification BibRef

Bandopadhyay, S.[Subhajit], Rastogi, A.[Anshu], Rascher, U.[Uwe], Rademske, P.[Patrick], Schickling, A.[Anke], Cogliati, S.[Sergio], Julitta, T.[Tommaso], Arthur, A.M.[Alasdair Mac], Hueni, A.[Andreas], Tomelleri, E.[Enrico], Celesti, M.[Marco], Burkart, A.[Andreas], Strózecki, M.[Marcin], Sakowska, K.[Karolina], Gabka, M.[Maciej], Rosadzinski, S.[Stanislaw], Sojka, M.[Mariusz], Iordache, M.D.[Marian-Daniel], Reusen, I.[Ils], Van Der Tol, C.[Christiaan], Damm, A.[Alexander], Schuettemeyer, D.[Dirk], Juszczak, R.[Radoslaw],
Hyplant-Derived Sun-Induced Fluorescence: A New Opportunity to Disentangle Complex Vegetation Signals from Diverse Vegetation Types,
RS(11), No. 14, 2019, pp. xx-yy.
DOI Link 1908

Derksen, D.[Dawa], Inglada, J.[Jordi], Michel, J.[Julien],
A Metric for Evaluating the Geometric Quality of Land Cover Maps Generated with Contextual Features from High-Dimensional Satellite Image Time Series without Dense Reference Data,
RS(11), No. 16, 2019, pp. xx-yy.
DOI Link 1909

Zhang, Q.[Qi], Zhang, P.L.[Peng-Lin], Xiao, Y.[Yao],
A Modeling and Measurement Approach for the Uncertainty of Features Extracted from Remote Sensing Images,
RS(11), No. 16, 2019, pp. xx-yy.
DOI Link 1909

Morales-Barquero, L.[Lucia], Lyons, M.B.[Mitchell B.], Phinn, S.R.[Stuart R.], Roelfsema, C.M.[Chris M.],
Trends in Remote Sensing Accuracy Assessment Approaches in the Context of Natural Resources,
RS(11), No. 19, 2019, pp. xx-yy.
DOI Link 1910

Zhang, X.K.[Xiao-Kang], Shi, W.Z.[Wen-Zhong], Lv, Z.Y.[Zhi-Yong],
Uncertainty Assessment in Multitemporal Land Use/Cover Mapping with Classification System Semantic Heterogeneity,
RS(11), No. 21, 2019, pp. xx-yy.
DOI Link 1911

Fisk, C.[Claire], Clarke, K.D.[Kenneth D.], Delean, S.[Steven], Lewis, M.M.[Megan M.],
Distinguishing Photosynthetic and Non-Photosynthetic Vegetation: How Do Traditional Observations and Spectral Classification Compare?,
RS(11), No. 21, 2019, pp. xx-yy.
DOI Link 1911

Halladin-Dabrowska, A.[Anna], Kania, A.[Adam], Kopec, D.[Dominik],
The t-SNE Algorithm as a Tool to Improve the Quality of Reference Data Used in Accurate Mapping of Heterogeneous Non-Forest Vegetation,
RS(12), No. 1, 2019, pp. xx-yy.
DOI Link 2001

Yang, X.[Xue], Li, F.[Feng], Xin, L.[Lei], Lu, X.T.[Xiao-Tian], Lu, M.[Ming], Zhang, N.[Nan],
An Improved Mapping with Super-Resolved Multispectral Images for Geostationary Satellites,
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DOI Link 2002
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Burdziakowski, P.[Pawel],
Increasing the Geometrical and Interpretation Quality of Unmanned Aerial Vehicle Photogrammetry Products using Super-Resolution Algorithms,
RS(12), No. 5, 2020, pp. xx-yy.
DOI Link 2003

Alba-Fernández, M.V.[María V.], Ariza-López, F.J.[Francisco J.], Rodríguez-Avi, J.[José], García-Balboa, J.L.[José L.],
Statistical Methods for Thematic-Accuracy Quality Control Based on an Accurate Reference Sample,
RS(12), No. 5, 2020, pp. xx-yy.
DOI Link 2003

Ma, X.L.[Xuan-Long], Huete, A.[Alfredo], Tran, N.N.[Ngoc Nguyen], Bi, J.[Jian], Gao, S.[Sicong], Zeng, Y.[Yelu],
Sun-Angle Effects on Remote-Sensing Phenology Observed and Modelled Using Himawari-8,
RS(12), No. 8, 2020, pp. xx-yy.
DOI Link 2004
Sun angle effects on results. BibRef

Hinojo-Hinojo, C.[César], Goulden, M.L.[Michael L.],
Plant Traits Help Explain the Tight Relationship between Vegetation Indices and Gross Primary Production,
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DOI Link 2005

Radosavljevic, M.[Miloš], Brkljac, B.[Branko], Lugonja, P.[Predrag], Crnojevic, V.[Vladimir], Trpovski, Ž.[Željen], Xiong, Z.X.[Zi-Xiang], Vukobratovic, D.[Dejan],
Lossy Compression of Multispectral Satellite Images with Application to Crop Thematic Mapping: A HEVC Comparative Study,
RS(12), No. 10, 2020, pp. xx-yy.
DOI Link 2006

Ma, L.L.[Ling-Ling], Zhao, Y.G.[Yong-Guang], Woolliams, E.R.[Emma R.], Dai, C.H.[Cai-Hong], Wang, N.[Ning], Liu, Y.[Yaokai], Li, L.[Ling], Wang, X.H.[Xin-Hong], Gao, C.X.[Cai-Xia], Li, C.R.[Chuan-Rong], Tang, L.[Lingli],
Uncertainty Analysis for RadCalNet Instrumented Test Sites Using the Baotou Sites BTCN and BSCN as Examples,
RS(12), No. 11, 2020, pp. xx-yy.
DOI Link 2006

Zhang, Z.J.[Zhi-Jiang], Zhao, L.[Lin], Lin, A.[Aiwen],
Evaluating the Performance of Sentinel-3A OLCI Land Products for Gross Primary Productivity Estimation Using AmeriFlux Data,
RS(12), No. 12, 2020, pp. xx-yy.
DOI Link 2006

Apostolopoulos, D.N.[Dionysios N.], Nikolakopoulos, K.G.[Konstantinos G.],
Assessment and Quantification of the Accuracy of Low-and High-Resolution Remote Sensing Data for Shoreline Monitoring,
IJGI(9), No. 6, 2020, pp. xx-yy.
DOI Link 2006

Maxwell, A.E.[Aaron E.], Warner, T.A.[Timothy A.],
Thematic Classification Accuracy Assessment with Inherently Uncertain Boundaries: An Argument for Center-Weighted Accuracy Assessment Metrics,
RS(12), No. 12, 2020, pp. xx-yy.
DOI Link 2006

Cheng, K.S., Ling, J.Y., Lin, T.W., Liu, Y.T., Shen, Y.C., Kono, Y.,
A New Thinking of LULC Classification Accuracy Assessment,
DOI Link 1912
Land-Use/Land-Cover BibRef

Oxoli, D., Bratic, G., Wu, H., Brovelli, M.A.[Maria Antonia],
Extending Accuracy Assessment Procedures of Global Coverage Land Cover Maps Through Spatial Association Analysis,
DOI Link 1912

Christovam, L.E., Pessoa, G.G., Shimabukuro, M.H., Galo, M.L.B.T.,
Land Use and Land Cover Classification Using Hyperspectral Imagery: Evaluating The Performance of Spectral Angle Mapper, Support Vector Machine and Random Forest,
DOI Link 1912

Yang, C.H., Soergel, U.,
Evaluation of a Psi-based Change Detection Regarding Simulation, Comparison, and Application,
DOI Link 1912

Hasan, M., Ullah, S., Khan, M.J., Khurshid, K.,
Comparative Analysis of Svm, Ann and Cnn for Classifying Vegetation Species Using Hyperspectral Thermal Infrared Data,
DOI Link 1912

Tuzcu, A., Taskin, G., Musaoglu, N.,
Comparison of Object Based Machine Learning Classifications Of Planetscope and Worldview-3 Satellite Images for Land Use / Cover,
DOI Link 1912

Muhammad, U., Wang, W., Chattha, S.P., Ali, S.,
Pre-trained VGGNet Architecture for Remote-Sensing Image Scene Classification,
Feature extraction, Correlation, Support vector machines, Covariance matrices, Remote sensing, Fuses, Semantics BibRef

Wang, X., Yan, H., Huo, C., Yu, J., Pant, C.,
Enhancing Pix2Pix for Remote Sensing Image Classification,
Generators, Remote sensing, Support vector machines, Image reconstruction, Training, Feature extraction, Buildings, Pix2Pix BibRef

Vicente-Guijalba, F., Duro, J., Notarnicola, C., Jacob, A., Sonnenschein, R., Mallorquí, J.J., López-Martínez, C., Ziólkowski, D., Hoscilo, A., Dabrowska-Zielinska, K., Bochenek, Z., Pottier, E., Lavalle, M., Lopez-Sanchez, J.M., Engdahl, M.,
Assessing hypertemporal SENTINEL-1 COHERENCE maps for LAND COVER monitoring,
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Nakada, R.[Ryuji], Takigawa, M.[Masanori], Ohga, T.[Tomowo], Fujii, N.[Noritsuna],
Verification Of Potency Of Aerial Digital Oblique Cameras For Aerial Photogrammetry In Japan,
ISPRS16(B1: 63-68).
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Gokaraju, B., Bhushan, S., Anantharaj, V., Turlapaty, A.C., Doss, D.A.,
Comprehensive review of evolution of satellite sensor specifications against speedup performance of pattern recognition algorithms in remote sensing,
artificial satellites BibRef

Braun, A.C., Weinmann, M., Keller, S., Müller, R., Reinartz, P., Hinz, S.,
The ENMAP Contest: Developing and Comparing Classification Approaches for the Environmental Mapping and Analysis Programme - Dataset and First Results,
DOI Link 1602

Regnauld, N.,
Generalisation and Data Quality,
DOI Link 1602

Yilmaz, C., Cömert, Ç.,
Ontology Based Quality Evaluation for Spatial Data,
DOI Link 1602

Jiao, W., Long, T., Yang, G., He, G.,
A New Method for Geometric Quality Evaluation of Remote Sensing Image Based on Information Entropy,
DOI Link 1411

Costantino, D., Angelini, M.G.,
Qualitative and Quantitative Evaluation of the Luminance of Laser Scanner Radiation for the Classification of Materials,
HTML Version. 1311

Bahmanyar, R.[Reza], Datcu, M.[Mihai],
Measuring the semantic gap based on a communication channel model,
Communication Channel BibRef

Bahmanyar, R., Rigoll, G., Datcu, M.,
A Clustering-Based Approach for Evaluation of EO Image Indexing,
HTML Version. 1311

Gülch, E., Al-Ghorani, N., Quedenfeldt, B., Braun, J.,
Evaluation and Development of E-learning Tools and Methods In Digital Photogrammetry and Remote Sensing for Non Experts From Academia And Industry,
DOI Link 1209

Teng, W.Y.[Wei-Yuan], Zhang, J.[Jing], Zhou, C.P.[Chun-Ping], Liu, X.M.[Xiao-Meng], Wu, Q.[Qiong], Jiang, M.B.[Min-Bin],
Research on Super-Resolution Objective Evaluation Index System of Visible Light Image,
What it really means to have higher resolution data for remote sensing. BibRef

Ji, X.[Xiaole], Bo, Y.C.[Yan-Chen],
Uncertainty Measures for Assessing the Attribute Accuracy of Objected-Based Classification of Remotely Sensed Imagery,
Evaluation of object level recognition different from pixel level. BibRef

Chapter on Remote Sensing, Cartography, Aerial Images, Buildings, Roads, Terrain, ATR continues in
Gravity Measurements .

Last update:Jul 10, 2020 at 16:03:35