22.1.4 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

Meroni, M., Atzberger, C., Vancutsem, C., Gobron, N., Baret, F., Lacaze, R., Eerens, H., Leo, O.,
Evaluation of Agreement Between Space Remote Sensing SPOT-VEGETATION fAPAR Time Series,
GeoRS(51), No. 4, April 2013, pp. 1951-1962.

Garcia-Santos, V., Valor, E., Caselles, V., Mira, M., Galve, J.M., Coll, C.,
Evaluation of Different Methods to Retrieve the Hemispherical Downwelling Irradiance in the Thermal Infrared Region for Field Measurements,
GeoRS(51), No. 4, April 2013, pp. 2155-2165.

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

Qian, Y.[Yuguo], Zhou, W.Q.[Wei-Qi], Yan, J.L.[Jing-Li], Li, W.F.[Wei-Feng], Han, L.J.[Li-Jian],
Comparing Machine Learning Classifiers for Object-Based Land Cover Classification Using Very High Resolution Imagery,
RS(7), No. 1, 2014, pp. 153-168.
DOI Link 1502

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

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], Leitão, 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

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

Zhong, Y.F.[Yan-Fei], Zhu, Q.[Qiqi], Zhang, L.P.[Liang-Pei],
Scene Classification Based on the Multifeature Fusion Probabilistic Topic Model for High Spatial Resolution Remote Sensing Imagery,
GeoRS(53), No. 11, November 2015, pp. 6207-6222.
feature extraction BibRef

Zhu, Q.[Qiqi], Zhong, Y.F.[Yan-Fei], Zhang, L.P.[Liang-Pei],
Scene Classfication Based On The Semantic-feature Fusion Fully Sparse Topic Model For High Spatial Resolution Remote Sensing Imagery,
ISPRS16(B7: 451-457).
DOI Link 1610

Zhao, B.[Bei], Zhong, Y.F.[Yan-Fei], Xia, G.S.[Gui-Song], Zhang, L.P.[Liang-Pei],
Dirichlet-Derived Multiple Topic Scene Classification Model for High Spatial Resolution Remote Sensing Imagery,
GeoRS(54), No. 4, April 2016, pp. 2108-2123.
Buildings BibRef

Hu, J.W.[Jing-Wen], Xia, G.S.[Gui-Song], Hu, F.[Fan], Zhang, L.P.[Liang-Pei],
A Comparative Study of Sampling Analysis in the Scene Classification of Optical High-Spatial Resolution Remote Sensing Imagery,
RS(7), No. 11, 2015, pp. 14988.
DOI Link 1512

Hu, F.[Fan], Xia, G.S.[Gui-Song], Hu, J.W.[Jing-Wen], Zhong, Y.F.[Yan-Fei], Xu, K.[Kan],
Fast Binary Coding for the Scene Classification of High-Resolution Remote Sensing Imagery,
RS(8), No. 7, 2016, pp. 555.
DOI Link 1608

Zhao, B.[Bei], Zhong, Y.F.[Yan-Fei], Zhang, L.P.[Liang-Pei],
A spectral-structural bag-of-features scene classifier for very high spatial resolution remote sensing imagery,
PandRS(116), No. 1, 2016, pp. 73-85.
Elsevier DOI 1604
Scene classification 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], Peña, 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], Muñoz-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], Guimarães, 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.[Agnès], 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

Percivall, G.[George], Idol, T.[Terry], Alameh, N.[Nadine], Harrison, J.[Jeff],
Innovation in OGC: The Interoperability Program,
IJGI(4), No. 4, 2015, pp. 2362.
DOI Link 1601
Open Geospatial Consortium (OGC). BibRef

Ge, Y.[Yong], Jiang, Y.[Yu], Chen, Y.H.[Yue-Hong], Stein, A.[Alfred], Jiang, D.[Dong], Jia, Y.X.[Yuan-Xin],
Designing an Experiment to Investigate Subpixel Mapping as an Alternative Method to Obtain Land Use/Land Cover Maps,
RS(8), No. 5, 2016, pp. 360.
DOI Link 1606

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.[Görres], 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

Peng, S.[Shi], Wen, J.G.[Jian-Guang], Xiao, Q.[Qing], You, D.Q.[Dong-Qin], Dou, B.C.[Bao-Cheng], Liu, Q.A.[Qi-Ang], Tang, Y.[Yong],
Multi-Staged NDVI Dependent Snow-Free Land-Surface Shortwave Albedo Narrowband-to-Broadband (NTB) Coefficients and Their Sensitivity Analysis,
RS(9), No. 1, 2017, pp. xx-yy.
DOI Link 1702

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

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).
DOI Link 1610

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

Sharma, J.B., Hulsey, D.,
Integrating the UAS in Undergraduate Teaching and Research: Opportunities and Challenges at University of North Georgia,
DOI Link 1411

Allen, J.E., Cruz, C.,
Professional Development in Remote Sensing for Community College Instructors,
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

Ito, Y., Ikemitsu, H., Nango, K.,
Development And Evaluation Of Science And Technology Education Program Using Interferometric SAR,
ISPRS16(B6: 123-129).
DOI Link 1610

Ito, Y., Teramoto, Y., Abe, K.,
Development and Evaluation of Technology Education Using Earth Observation Technique,
DOI Link 1209

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 Cartography, Aerial Images, Remote Sensing, Buildings, Roads, Terrain, ATR continues in
Land Cover, Land Use, General Problems, Remote Sensing .

Last update:Mar 13, 2017 at 16:25:24