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, FVC.
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.

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Atoum, Y.[Yousef], Afridi, M.J.[Muhammad Jamal], Liu, X.M.[Xiao-Ming], McGrath, J.M.[J. Mitchell], Hanson, L.E.[Linda E.],
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Sawada, Y., Tsutsui, H., Koike, T., Rasmy, M., Seto, R., Fujii, H.,
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Chang, T.[Tommy], Comandur, B.[Bharath], Park, J.[Johnny], Kak, A.C.[Avinash C.],
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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],
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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],
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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],
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Volpi, M., Tuia, D.[Devis],
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GeoRS(55), No. 2, February 2017, pp. 881-893.
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geophysical image processing BibRef

Tan, Q.Y.[Qiao-Yu], Liu, Y.[Yezi], Chen, X.[Xia], Yu, G.X.[Guo-Xian],
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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,
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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.
IEEE DOI 1807
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.
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Lu, X., Zheng, X., Yuan, Y.,
Remote Sensing Scene Classification by Unsupervised Representation Learning,
GeoRS(55), No. 9, September 2017, pp. 5148-5157.
IEEE DOI 1709
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Santara, A., Mani, K., Hatwar, P., Singh, A., Garg, A., Padia, K., Mitra, P.,
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Marinoni, A., Iannelli, G.C., Gamba, P.,
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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.
IEEE DOI 1801
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,
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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.
IEEE DOI 1804
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], Muñoz-Marí, J.[Jordi], Laparra, V.[Valero], Campos-Taberner, M.[Manuel], Luengo, D.[David],
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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,
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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.
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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,
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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,
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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.
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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.
IEEE DOI 1901
Surface topography, Atmospheric modeling, Forestry, Vegetation, Scattering, Remote sensing, Canopy reflectance, sloped surface BibRef

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Classification of High-Mountain Vegetation Communities within a Diverse Giant Mountains Ecosystem Using Airborne APEX Hyperspectral Imagery,
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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,
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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.
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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.
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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.
IEEE DOI 1808
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,
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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,
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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,
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Garcia-Salgado, B.P.[Beatriz P.], Ponomaryov, V.I.[Volodymyr I.], Sadovnychiy, S.[Sergiy], Robles-Gonzalez, M.[Marco],
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Springer DOI 1811
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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,
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Campos, J.C.[João 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],
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Gaetano, R.[Raffaele], Ienco, D.[Dino], Ose, K.[Kenji], Cresson, R.[Remi],
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Zhang, W.[Wei], Tang, P.[Ping], Zhao, L.J.[Li-Jun],
Remote Sensing Image Scene Classification Using CNN-CapsNet,
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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,
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Park, S.E.[Sang-Eun], Jung, Y.T.[Yoon Taek], Cho, J.H.[Jae-Hyoung], Moon, H.[Hyoi], Han, S.H.[Seung-Hoon],
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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,
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Ratajczak, R., Crispim-Junior, C.F., Faure, E., Fervers, B., Tougne, L.,
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IEEE DOI 1906
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],
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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.],
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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.],
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Fang, J.[Jie], Yuan, Y.[Yuan], Lu, X.Q.[Xiao-Qiang], Feng, Y.C.[Ya-Chuang],
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IEEE DOI 1910
convolutional neural nets, feature extraction, geophysical image processing, image classification, space domain BibRef

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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],
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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],
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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],
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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],
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RS(12), No. 6, 2020, pp. xx-yy.
DOI Link 2003
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Hou, W.J.[Wen-Juan], Gao, J.B.[Jiang-Bo],
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RS(12), No. 7, 2020, pp. xx-yy.
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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.
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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,
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Chen, C.P.J.[Chun-Peng James], Zhang, Z.W.[Zhi-Wu],
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RS(12), No. 11, 2020, pp. xx-yy.
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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
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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
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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
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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
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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
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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
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Mboga, N.[Nicholus], Grippa, T.[Tais], Georganos, S.[Stefanos], Vanhuysse, S.[Sabine], Smets, B.[Benoît], Dewitte, O.[Olivier], Wolff, E.[Elèonore], 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
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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
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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
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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.
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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
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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
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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
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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.
IEEE DOI 2104
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.
IEEE DOI 2112
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
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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
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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
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Sainos-Vizuett, M.[Michelle], Lopez-Nava, I.H.[Irvin Hussein],
Satellite Imagery Classification Using Shallow and Deep Learning Approaches,
MCPR21(163-172).
Springer DOI 2108
BibRef

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
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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
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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
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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
BibRef

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
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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
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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
BibRef

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
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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
BibRef

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
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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
BibRef

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
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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
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Chandler, C.J.[Chris J.], Ávila-Mosqueda, S.V.[Silvia Valery], Salas-Acosta, E.R.[Evelyn Raquel], Magaña-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
BibRef

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
BibRef

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
BibRef

Zhao, J.Q.[Jun-Qi], Du, D.S.[Dong-Sheng], Chen, L.[Lifu], Liang, X.J.[Xiu-Juan], Chen, H.[Haoda], Jin, Y.C.[Yu-Chen],
HA-Net for Bare Soil Extraction Using Optical Remote Sensing Images,
RS(16), No. 16, 2024, pp. 3088.
DOI Link 2408
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Rad, R.[Ryan],
Vision Transformer for Multispectral Satellite Imagery: Advancing Landcover Classification*,
WACV24(8161-8168)
IEEE DOI 2404
Multispectral imaging, Climate change, Environmental monitoring, Ecology, Image recognition, Remote sensing, Globalization, Remote Sensing BibRef

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,
IVCNZ23(1-6)
IEEE DOI 2403
Training, Photography, Analytical models, Annotations, Semantic segmentation, Supervised learning, Soil BibRef