22.2.2 Land Cover, Land Use, Very High Resolution, High Spatial Resolution

Chapter Contents (Back)
High Resolution. VHR, HR.
See also Subpixel Target, Subpixel Land Use, Tiny Objects.

Ehlers, M.[Manfred], Gähler, M.[Monika], Janowsky, R.[Ronald],
Automated analysis of ultra high resolution remote sensing data for biotope type mapping: new possibilities and challenges,
PandRS(57), No. 5-6, April 2003, pp. 315-326.
Elsevier DOI 0307

Qian, Y.G.[Yu-Guo], 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

Baraldi, A., Boschetti, L., Humber, M.L.,
Probability Sampling Protocol for Thematic and Spatial Quality Assessment of Classification Maps Generated From Spaceborne/Airborne Very High Resolution Images,
GeoRS(52), No. 1, January 2014, pp. 701-760.
decision trees BibRef

Lv, Z.Y.[Zhi-Yong], He, H.Q.[Hai-Qing], Benediktsson, J.A.[Jón Atli], Huang, H.[Hong],
A Generalized Image Scene Decomposition-Based System for Supervised Classification of Very High Resolution Remote Sensing Imagery,
RS(8), No. 10, 2016, pp. 814.
DOI Link 1609
Regions based for classification. BibRef

Shi, C.[Cheng], Lv, Z.Y.[Zhi-Yong], Yang, X.H.[Xiu-Hong], Xu, P.F.[Peng-Fei], Bibi, I.[Irfana],
Hierarchical Multi-View Semi-Supervised Learning for Very High-Resolution Remote Sensing Image Classification,
RS(12), No. 6, 2020, pp. xx-yy.
DOI Link 2003

Witharana, C.[Chandi], Lynch, H.J.[Heather J.],
An Object-Based Image Analysis Approach for Detecting Penguin Guano in very High Spatial Resolution Satellite Images,
RS(8), No. 5, 2016, pp. 375.
DOI Link 1606

Lv, Z.Y.[Zhi-Yong], Shi, W.Z.[Wen-Zhong], Benediktsson, J.A.[Jón Atli], Ning, X.J.[Xiao-Juan],
Novel Object-Based Filter for Improving Land-Cover Classification of Aerial Imagery with Very High Spatial Resolution,
RS(8), No. 12, 2016, pp. 1023.
DOI Link 1612

Lv, Z.Y.[Zhi-Yong], Zhang, P.L.[Peng-Lin], Benediktsson, J.A.[Jón Atli],
Automatic Object-Oriented, Spectral-Spatial Feature Extraction Driven by Tobler's First Law of Geography for Very High Resolution Aerial Imagery Classification,
RS(9), No. 3, 2017, pp. xx-yy.
DOI Link 1704

Georganos, S.[Stefanos], Grippa, T.[Tais], Lennert, M.[Moritz], Vanhuysse, S.[Sabine], Johnson, B.A.[Brian Alan], Wolff, E.[Eléonore],
Scale Matters: Spatially Partitioned Unsupervised Segmentation Parameter Optimization for Large and Heterogeneous Satellite Images,
RS(10), No. 9, 2018, pp. xx-yy.
DOI Link 1810
For very high resolution, use regions (objects). BibRef

Marcos, D.[Diego], Volpi, M.[Michele], Kellenberger, B.[Benjamin], Tuia, D.[Devis],
Land cover mapping at very high resolution with rotation equivariant CNNs: Towards small yet accurate models,
PandRS(145), 2018, pp. 96-107.
Elsevier DOI 1810
Semantic labeling, Deep learning, Rotation invariance, Sub-decimeter resolution BibRef

Zhang, L., Bai, M., Liao, R., Urtasun, R., Marcos, D., Tuia, D., Kellenberger, B.,
Learning Deep Structured Active Contours End-to-End,
Buildings, Image segmentation, Active contours, Force, Training, Inference algorithms, Semantics BibRef

Liu, Y.C.[Yong-Cheng], Fan, B.[Bin], Wang, L.F.[Ling-Feng], Bai, J.[Jun], Xiang, S.M.[Shi-Ming], Pan, C.H.[Chun-Hong],
Semantic labeling in very high resolution images via a self-cascaded convolutional neural network,
PandRS(145), 2018, pp. 78-95.
Elsevier DOI 1810
Semantic labeling, Convolutional neural networks (CNNs), Multi-scale contexts, End-to-end BibRef

Pan, X.[Xin], Zhao, J.[Jian],
High-Resolution Remote Sensing Image Classification Method Based on Convolutional Neural Network and Restricted Conditional Random Field,
RS(10), No. 6, 2018, pp. xx-yy.
DOI Link 1806

Pan, X.[Xin], Zhang, C.[Ce], Xu, J.[Jun], Zhao, J.[Jian],
Simplified Object-Based Deep Neural Network for Very High Resolution Remote Sensing Image Classification,
PandRS(181), 2021, pp. 218-237.
Elsevier DOI 2110
CNN, Very high resolution, Semantic segmentation, Classification, OBIA BibRef

Pan, X.[Xin], Xu, J.[Jun], Zhao, J.[Jian], Li, X.F.[Xiao-Feng],
Hierarchical Object-Focused and Grid-Based Deep Unsupervised Segmentation Method for High-Resolution Remote Sensing Images,
RS(14), No. 22, 2022, pp. xx-yy.
DOI Link 2212

Hong, D.F.[Dan-Feng], Yokoya, N.[Naoto], Ge, N.[Nan], Chanussot, J.[Jocelyn], Zhu, X.X.[Xiao Xiang],
Learnable manifold alignment (LeMA): A semi-supervised cross-modality learning framework for land cover and land use classification,
PandRS(147), 2019, pp. 193-205.
Elsevier DOI 1901
Cross-modality, Graph learning, Hyperspectral, Manifold alignment, Multispectral, Remote sensing, Semi-supervised learning BibRef

Dong, X.Y.[Xiao-Yu], Yokoya, N.[Naoto], Wang, L.G.[Long-Guang], Uezato, T.[Tatsumi],
Learning Mutual Modulation for Self-supervised Cross-Modal Super-Resolution,
Springer DOI 2211

Nogueira, K.[Keiller], Dalla Mura, M., Chanussot, J.[Jocelyn], Schwartz, W.R., dos Santos, J.A.[Jefersson A.],
Learning to Semantically Segment High-Resolution Remote Sensing Images,
Context, Feature extraction, Image segmentation, Machine learning, Remote sensing, Semantics, Visualization, Deep Learning, Feature Learning, High-resolution Images, Land-cover Mapping, Pixel-wise Classification, Remote Sensing, Semantic, Segmentation BibRef

Luo, B.[Bin], Chanussot, J.[Jocelyn],
Geometrical features for the classification of very high resolution multispectral remote-sensing images,

Yang, X.[Xuan], Li, S.S.[Shan-Shan], Chen, Z.C.[Zheng-Chao], Chanussot, J.[Jocelyn], Jia, X.P.[Xiu-Ping], Zhang, B.[Bing], Li, B.P.[Bai-Peng], Chen, P.[Pan],
An attention-fused network for semantic segmentation of very-high-resolution remote sensing imagery,
PandRS(177), 2021, pp. 238-262.
Elsevier DOI 2106
Semantic segmentation, Deep learning, Very-high-resolution imagery, Attention-fused network, ISPRS, Convolutional neural network BibRef

Wang, Y.[Yuhao], Liang, B.X.[Bin-Xiu], Ding, M.[Meng], Li, J.Y.[Jiang-Yun],
Dense Semantic Labeling with Atrous Spatial Pyramid Pooling and Decoder for High-Resolution Remote Sensing Imagery,
RS(11), No. 1, 2018, pp. xx-yy.
DOI Link 1901

Wang, Y.[Yuhao], Chen, C.[Chen], Ding, M.[Meng], Li, J.Y.[Jiang-Yun],
Real-Time Dense Semantic Labeling with Dual-Path Framework for High-Resolution Remote Sensing Image,
RS(11), No. 24, 2019, pp. xx-yy.
DOI Link 1912

Flores, E.[Eliezer], Zortea, M.[Maciel], Scharcanski, J.[Jacob],
Dictionaries of deep features for land-use scene classification of very high spatial resolution images,
PR(89), 2019, pp. 32-44.
Elsevier DOI 1902
Deep learning, Dictionary learning, Feature learning, Land-use classification, Sparse representation BibRef

Xu, L.[Lu], Ming, D.P.[Dong-Ping], Zhou, W.[Wen], Bao, H.Q.[Han-Qing], Chen, Y.Y.[Yang-Yang], Ling, X.[Xiao],
Farmland Extraction from High Spatial Resolution Remote Sensing Images Based on Stratified Scale Pre-Estimation,
RS(11), No. 2, 2019, pp. xx-yy.
DOI Link 1902

Mo, N.[Nan], Yan, L.[Li], Zhu, R.X.[Rui-Xi], Xie, H.[Hong],
Class-Specific Anchor Based and Context-Guided Multi-Class Object Detection in High Resolution Remote Sensing Imagery with a Convolutional Neural Network,
RS(11), No. 3, 2019, pp. xx-yy.
DOI Link 1902

Mboga, N.[Nicholus], Georganos, S.[Stefanos], Grippa, T.[Tais], Lennert, M.[Moritz], Vanhuysse, S.[Sabine], Wolff, E.[Eléonore],
Fully Convolutional Networks and Geographic Object-Based Image Analysis for the Classification of VHR Imagery,
RS(11), No. 5, 2019, pp. xx-yy.
DOI Link 1903

Geiß, C.[Christian], Pelizari, P.A.[Patrick Aravena], Blickensdörfer, L.[Lukas], Taubenböck, H.[Hannes],
Virtual Support Vector Machines with self-learning strategy for classification of multispectral remote sensing imagery,
PandRS(151), 2019, pp. 42-58.
Elsevier DOI 1904
Classification, Support Vector Machines, Self-learning, Active learning heuristics, Very high spatial resolution imagery BibRef

Zhang, H.[Heng], Eziz, A.[Anwar], Xiao, J.[Jian], Tao, S.L.[Sheng-Li], Wang, S.P.[Shao-Peng], Tang, Z.Y.[Zhi-Yao], Zhu, J.L.[Jiang-Ling], Fang, J.Y.[Jing-Yun],
High-Resolution Vegetation Mapping Using eXtreme Gradient Boosting Based on Extensive Features,
RS(11), No. 12, 2019, pp. xx-yy.
DOI Link 1907

Huang, H.[Hong], Xu, K.[Kejie],
Combing Triple-Part Features of Convolutional Neural Networks for Scene Classification in Remote Sensing,
RS(11), No. 14, 2019, pp. xx-yy.
DOI Link 1908

Zhang, Y.H.[Yi-Hang], Atkinson, P.M.[Peter M.], Li, X.D.[Xiao-Dong], Ling, F.[Feng], Wang, Q.M.[Qun-Ming], Du, Y.[Yun],
Learning-Based Spatial-Temporal Superresolution Mapping of Forest Cover With MODIS Images,
GeoRS(55), No. 1, January 2017, pp. 600-614.
vegetation mapping BibRef

Ling, F.[Feng], Zhang, Y.H.[Yi-Hang], Foody, G.M.[Giles M.], Li, X.D.[Xiao-Dong], Zhang, X.H.[Xiu-Hua], Fang, S.M.[Shi-Ming], Li, W.B.[Wen-Bo], Du, Y.[Yun],
Learning-Based Superresolution Land Cover Mapping,
GeoRS(54), No. 7, July 2016, pp. 3794-3810.
Algorithm design and analysis BibRef

Li, X.D.[Xiao-Dong], Ling, F.[Feng], Foody, G.M.[Giles M.], Ge, Y.[Yong], Zhang, Y.H.[Yi-Hang], Wang, L.H.[Li-Hui], Shi, L.F.[Ling-Fei], Li, X.Y.[Xin-Yan], Du, Y.[Yun],
Spatial-Temporal Super-Resolution Land Cover Mapping With a Local Spatial-Temporal Dependence Model,
GeoRS(57), No. 7, July 2019, pp. 4951-4966.
Spatial resolution, Remote sensing, Adaptation models, Graphical models, Distribution functions, Forestry, Image series, temporal dependence
See also Impervious Surface Change Mapping with an Uncertainty-Based Spatial-Temporal Consistency Model: A Case Study in Wuhan City Using Landsat Time-Series Datasets from 1987 to 2016. BibRef

Chen, Y.H.[Yue-Hong], Zhou, Y.[Ya'nan], Ge, Y.[Yong], An, R.[Ru], Chen, Y.[Yu],
Enhancing Land Cover Mapping through Integration of Pixel-Based and Object-Based Classifications from Remotely Sensed Imagery,
RS(10), No. 1, 2018, pp. xx-yy.
DOI Link 1802

See also Super-Resolution Mapping of Impervious Surfaces from Remotely Sensed Imagery with Points-of-Interest. BibRef

Chen, Y.H.[Yue-Hong], Ge, Y.[Yong], Heuvelink, G.B.M.[Gerard B.M.], An, R.[Ru], Chen, Y.[Yu],
Object-Based Superresolution Land-Cover Mapping From Remotely Sensed Imagery,
GeoRS(56), No. 1, January 2018, pp. 328-340.
geophysical image processing, image classification, land cover, terrain mapping, advanced object-based classification, superresolution mapping (SRM) BibRef

Jia, Y.I.[Yuan-In], Ge, Y.[Yong], Chen, Y.H.[Yue-Hong], Li, S.P.[San-Ping], Heuvelink, G.B.M.[Gerard B.M.], Ling, F.[Feng],
Super-Resolution Land Cover Mapping Based on the Convolutional Neural Network,
RS(11), No. 15, 2019, pp. xx-yy.
DOI Link 1908

Li, X.D.[Xiao-Dong], Ling, F.[Feng], Du, Y.[Yun], Feng, Q.[Qi], Zhang, Y.H.[Yi-Hang],
A spatial-temporal Hopfield neural network approach for super-resolution land cover mapping with multi-temporal different resolution remotely sensed images,
PandRS(93), No. 1, 2014, pp. 76-87.
Elsevier DOI 1407
Land cover BibRef

Li, X.D.[Xiao-Dong], Ling, F.[Feng], Foody, G.M., Du, Y.[Yun],
A Superresolution Land-Cover Change Detection Method Using Remotely Sensed Images With Different Spatial Resolutions,
GeoRS(54), No. 7, July 2016, pp. 3822-3841.
Earth BibRef

Ling, F., Foody, G.M., Ge, Y., Li, X.D.[Xiao-Dong], Du, Y.,
An Iterative Interpolation Deconvolution Algorithm for Superresolution Land Cover Mapping,
GeoRS(54), No. 12, December 2016, pp. 7210-7222.
geophysical image processing BibRef

Ling, F.[Feng], Foody, G.M.[Giles M.], Li, X.D.[Xiao-Dong], Zhang, Y.H.[Yi-Hang], Du, Y.[Yun],
Assessing a Temporal Change Strategy for Sub-Pixel Land Cover Change Mapping from Multi-Scale Remote Sensing Imagery,
RS(8), No. 8, 2016, pp. 642.
DOI Link 1609

Ling, F.[Feng], Du, Y.[Yun], Li, X.D.[Xiao-Dong], Zhang, Y.H.[Yi-Hang], Xiao, F.[Fei], Fang, S.M.[Shi-Ming], Li, W.B.[Wen-Bo],
Superresolution Land Cover Mapping With Multiscale Information by Fusing Local Smoothness Prior and Downscaled Coarse Fractions,
GeoRS(52), No. 9, Sept 2014, pp. 5677-5692.
land cover BibRef

Guo, J.[Jifa], Du, S.H.[Shi-Hong], Huo, H.Y.[Hong-Yuan], Du, S.J.[Shou-Ji], Zhang, X.Y.[Xiu-Yuan],
Modelling the Spectral Uncertainty of Geographic Features in High-Resolution Remote Sensing Images: Semi-Supervising and Weighted Interval Type-2 Fuzzy C-Means Clustering,
RS(11), No. 15, 2019, pp. xx-yy.
DOI Link 1908

Liu, X.L.[Xin-Long], He, C.[Chu], Xiong, D.[Dehui], Liao, M.S.[Ming-Sheng],
Pattern Statistics Network for Classification of High-Resolution SAR Images,
RS(11), No. 16, 2019, pp. xx-yy.
DOI Link 1909

Stoian, A.[Andrei], Poulain, V.[Vincent], Inglada, J.[Jordi], Poughon, V.[Victor], Derksen, D.[Dawa],
Land Cover Maps Production with High Resolution Satellite Image Time Series and Convolutional Neural Networks: Adaptations and Limits for Operational Systems,
RS(11), No. 17, 2019, pp. xx-yy.
DOI Link 1909

Zhu, R.X.[Rui-Xi], Yan, L.[Li], Mo, N.[Nan], Liu, Y.[Yi],
Retraction: Attention-Based Deep Feature Fusion for the Scene Classification of High-Resolution Remote Sensing Images,
RS(11), No. 17, 2019, pp. xx-yy.
DOI Link 1909
And: Retraction: RS(12), No. 4, 2020, pp. xx-yy.
DOI Link 2003

Yue, K.[Kai], Yang, L.[Lei], Li, R.[Ruirui], Hu, W.[Wei], Zhang, F.[Fan], Li, W.[Wei],
TreeUNet: Adaptive Tree convolutional neural networks for subdecimeter aerial image segmentation,
PandRS(156), 2019, pp. 1-13.
Elsevier DOI 1909
Aerial imagery, Semantic segmentation, Tree structures, Adaptive network, ISPRS, CNN BibRef

Kiani, A.[Abbas], Ebadi, H.[Hamid], Ahmadi, F.F.[Farshid Farnood],
Development of an Object-Based Interpretive System Based on Weighted Scoring Method in a Multi-Scale Manner,
IJGI(8), No. 9, 2019, pp. xx-yy.
DOI Link 1909

Lv, Z.Y.[Zhi-Yong], Li, G.F.[Guang-Fei], Chen, Y.X.[Yi-Xiang], Benediktsson, J.A.[Jón Atli],
Novel Multi-Scale Filter Profile-Based Framework for VHR Remote Sensing Image Classification,
RS(11), No. 18, 2019, pp. xx-yy.
DOI Link 1909

Lei, T.[Tao], Li, L.Z.[Lin-Ze], Lv, Z.Y.[Zhi-Yong], Zhu, M.Z.[Ming-Zhe], Du, X.G.[Xiao-Gang], Nandi, A.K.[Asoke K.],
Multi-Modality and Multi-Scale Attention Fusion Network for Land Cover Classification from VHR Remote Sensing Images,
RS(13), No. 18, 2021, pp. xx-yy.
DOI Link 2109

Li, H.P.[Hua-Peng], Zhang, C.[Ce], Zhang, S.Q.[Shu-Qing], Atkinson, P.M.[Peter M.],
A hybrid OSVM-OCNN Method for Crop Classification from Fine Spatial Resolution Remotely Sensed Imagery,
RS(11), No. 20, 2019, pp. xx-yy.
DOI Link 1910

Abdollahnejad, A.[Azadeh], Panagiotidis, D.[Dimitrios], Bílek, L.[Lukáš],
An Integrated GIS and Remote Sensing Approach for Monitoring Harvested Areas from Very High-Resolution, Low-Cost Satellite Images,
RS(11), No. 21, 2019, pp. xx-yy.
DOI Link 1911

Li, Y.S.[Yan-Shan], Xu, J.J.[Jian-Jie], Xia, R.J.[Rong-Jie], Wang, X.C.[Xian-Chen], Xie, W.X.[Wei-Xin],
A two-stage framework of target detection in high-resolution hyperspectral images,
SIViP(13), No. 7, October 2019, pp. 1339-1346.
Springer DOI 1911

Tong, H.J.[Heng-Jian], Tong, F.[Fei], Zhou, W.[Wei], Zhang, Y.[Yun],
Purifying SLIC Superpixels to Optimize Superpixel-Based Classification of High Spatial Resolution Remote Sensing Image,
RS(11), No. 22, 2019, pp. xx-yy.
DOI Link 1911

Wu, Y.D.[Ying-Dan], Di, L.P.[Li-Ping], Ming, Y.[Yang], Lv, H.[Hui], Tan, H.[Han],
High-Resolution Optical Remote Sensing Image Registration via Reweighted Random Walk Based Hyper-Graph Matching,
RS(11), No. 23, 2019, pp. xx-yy.
DOI Link 1912

Li, E.[Erzhu], Samat, A.[Alim], Liu, W.[Wei], Lin, C.[Cong], Bai, X.[Xuyu],
High-Resolution Imagery Classification Based on Different Levels of Information,
RS(11), No. 24, 2019, pp. xx-yy.
DOI Link 1912

Zhang, G.[Gang], Lei, T.[Tao], Cui, Y.[Yi], Jiang, P.[Ping],
A Dual-Path and Lightweight Convolutional Neural Network for High-Resolution Aerial Image Segmentation,
IJGI(8), No. 12, 2019, pp. xx-yy.
DOI Link 1912

Zhang, S.Y.[Shu-Yu], Li, C.R.[Chuan-Rong], Qiu, S.[Shi], Gao, C.X.[Cai-Xia], Zhang, F.[Feng], Du, Z.H.[Zhen-Hong], Liu, R.[Renyi],
EMMCNN: An ETPS-Based Multi-Scale and Multi-Feature Method Using CNN for High Spatial Resolution Image Land-Cover Classification,
RS(12), No. 1, 2019, pp. xx-yy.
DOI Link 2001

Wu, T.J.[Tian-Jun], Luo, J.C.[Jian-Cheng], Zhou, Y.N.[Ya-Nan], Wang, C.P.[Chang-Peng], Xi, J.B.[Jiang-Bo], Fang, J.[Jianwu],
Geo-Object-Based Land Cover Map Update for High-Spatial-Resolution Remote Sensing Images via Change Detection and Label Transfer,
RS(12), No. 1, 2020, pp. xx-yy.
DOI Link 2001

Gong, Y., Xiao, Z., Tan, X., Sui, H., Xu, C., Duan, H., Li, D.,
Context-Aware Convolutional Neural Network for Object Detection in VHR Remote Sensing Imagery,
GeoRS(58), No. 1, January 2020, pp. 34-44.
Feature extraction, Object detection, Proposals, Semantics, Context modeling, Convolutional codes, object detection BibRef

Zhang, X.[Xin], Han, L.X.[Liang-Xiu], Han, L.H.[Liang-Hao], Zhu, L.[Liang],
How Well Do Deep Learning-Based Methods for Land Cover Classification and Object Detection Perform on High Resolution Remote Sensing Imagery?,
RS(12), No. 3, 2020, pp. xx-yy.
DOI Link 2002

Liang, W.W.[Wan-Wan], Abidi, M.[Mongi], Carrasco, L.[Luis], McNelis, J.[Jack], Tran, L.[Liem], Li, Y.K.[Ying-Kui], Grant, J.[Jerome],
Mapping Vegetation at Species Level with High-Resolution Multispectral and Lidar Data Over a Large Spatial Area: A Case Study with Kudzu,
RS(12), No. 4, 2020, pp. xx-yy.
DOI Link 2003

Lin, W.J.[Wen-Jie], Li, Y.[Yu],
Parallel Regional Segmentation Method of High-Resolution Remote Sensing Image Based on Minimum Spanning Tree,
RS(12), No. 5, 2020, pp. xx-yy.
DOI Link 2003

Pan, X.[Xin], Zhao, J.[Jian], Xu, J.[Jun],
An End-to-End and Localized Post-Processing Method for Correcting High-Resolution Remote Sensing Classification Result Images,
RS(12), No. 5, 2020, pp. xx-yy.
DOI Link 2003

Meier, E.S.[Eliane Seraina], Indermaur, A.[Alexander], Ginzler, C.[Christian], Psomas, A.[Achilleas],
An Effective Way to Map Land-Use Intensity with a High Spatial Resolution Based on Habitat Type and Environmental Data,
RS(12), No. 6, 2020, pp. xx-yy.
DOI Link 2003

Chen, H.R.[Hong-Ruixuan], Wu, C.[Chen], Du, B.[Bo], Zhang, L.P.[Liang-Pei], Wang, L.[Le],
Change Detection in Multisource VHR Images via Deep Siamese Convolutional Multiple-Layers Recurrent Neural Network,
GeoRS(58), No. 4, April 2020, pp. 2848-2864.
Change detection (CD), deep siamese convolutional multiple-layers recurrent neural network, very-high-resolution (VHR) images BibRef

Zhang, X.G.[Xin-Gang], Yan, H.[Haowen], Zhang, L.M.[Li-Ming], Wang, H.[Hao],
High-Resolution Remote Sensing Image Integrity Authentication Method Considering Both Global and Local Features,
IJGI(9), No. 4, 2020, pp. xx-yy.
DOI Link 2005

Samasse, K.[Kaboro], Hanan, N.P.[Niall P.], Anchang, J.Y.[Julius Y.], Diallo, Y.[Yacouba],
A High-Resolution Cropland Map for the West African Sahel Based on High-Density Training Data, Google Earth Engine, and Locally Optimized Machine Learning,
RS(12), No. 9, 2020, pp. xx-yy.
DOI Link 2005

Zhao, X.L.[Xiao-Lei], Zhang, J.[Jing], Tian, J.[Jimiao], Zhuo, L.[Li], Zhang, J.[Jie],
Residual Dense Network Based on Channel-Spatial Attention for the Scene Classification of a High-Resolution Remote Sensing Image,
RS(12), No. 11, 2020, pp. xx-yy.
DOI Link 2006

Körez, A.[Atakan], Barisçi, N.[Necaattin], Çetin, A.[Aydin], Ergün, U.[Uçman],
Weighted Ensemble Object Detection with Optimized Coefficients for Remote Sensing Images,
IJGI(9), No. 6, 2020, pp. xx-yy.
DOI Link 2006

Ding, L., Zhang, J., Bruzzone, L.,
Semantic Segmentation of Large-Size VHR Remote Sensing Images Using a Two-Stage Multiscale Training Architecture,
GeoRS(58), No. 8, August 2020, pp. 5367-5376.
Semantics, Training, Image segmentation, Feature extraction, Remote sensing, Convolution, semantic segmentation BibRef

Song, A.[Ahram], Kim, Y.[Yongil], Han, Y.K.[You-Kyung],
Uncertainty Analysis for Object-Based Change Detection in Very High-Resolution Satellite Images Using Deep Learning Network,
RS(12), No. 15, 2020, pp. xx-yy.
DOI Link 2008

Li, Y., Melgani, F.[Farid], He, B.,
CSVM Architectures for Pixel-Wise Object Detection in High-Resolution Remote Sensing Images,
GeoRS(58), No. 9, September 2020, pp. 6059-6070.
Object detection, Convolution, Remote sensing, Feature extraction, Training, Image resolution, Support vector machines, very high resolution (VHR) BibRef

Qi, X.M.[Xiao-Man], Zhu, P.P.[Pan-Pan], Wang, Y.B.[Yue-Bin], Zhang, L.Q.[Li-Qiang], Peng, J.H.[Jun-Huan], Wu, M.F.[Meng-Fan], Chen, J.L.[Jia-Long], Zhao, X.D.[Xu-Dong], Zang, N.[Ning], Mathiopoulos, P.T.[P. Takis],
MLRSNet: A multi-label high spatial resolution remote sensing dataset for semantic scene understanding,
PandRS(169), 2020, pp. 337-350.
Elsevier DOI 2011
Multi-label image dataset, Semantic scene understanding, Convolutional Neural Network (CNN), Image classification, Image retrieval BibRef

Li, F.P.[Feng-Peng], Feng, R.[Ruyi], Han, W.[Wei], Wang, L.Z.[Li-Zhe],
High-Resolution Remote Sensing Image Scene Classification via Key Filter Bank Based on Convolutional Neural Network,
GeoRS(58), No. 11, November 2020, pp. 8077-8092.
Feature extraction, Task analysis, Data models, Remote sensing, Benchmark testing, Computational modeling, scene classification BibRef

Li, M.M.[Meng-Meng], Stein, A.[Alfred],
Mapping Land Use from High Resolution Satellite Images by Exploiting the Spatial Arrangement of Land Cover Objects,
RS(12), No. 24, 2020, pp. xx-yy.
DOI Link 2012

Qian, Y.G.[Yu-Guo], Zhou, W.Q.[Wei-Qi], Yu, W.J.[Wen-Juan], Han, L.J.[Li-Jian], Li, W.F.[Wei-Feng], Zhao, W.H.[Wen-Hui],
Integrating Backdating and Transfer Learning in an Object-Based Framework for High Resolution Image Classification and Change Analysis,
RS(12), No. 24, 2020, pp. xx-yy.
DOI Link 2012

Lv, Z., Li, G., Jin, Z., Benediktsson, J.A., Foody, G.M.,
Iterative Training Sample Expansion to Increase and Balance the Accuracy of Land Classification From VHR Imagery,
GeoRS(59), No. 1, January 2021, pp. 139-150.
Training, Sensors, Hyperspectral imaging, Iterative methods, Feature extraction, Land cover classification, very high-resolution remote-sensing image BibRef

Gao, H.[Han], Guo, J.H.[Jin-Hui], Guo, P.[Peng], Chen, X.[Xiuwan],
Classification of Very-High-Spatial-Resolution Aerial Images Based on Multiscale Features with Limited Semantic Information,
RS(13), No. 3, 2021, pp. xx-yy.
DOI Link 2102

Huang, Z.[Zhou], Chen, H.X.[Huai-Xin], Zhou, T.[Tao], Yang, Y.Z.[Yun-Zhi], Wang, C.Y.[Chang-Yin], Liu, B.Y.[Bi-Yuan],
Contrast-weighted dictionary learning based saliency detection for VHR optical remote sensing images,
PR(113), 2021, pp. 107757.
Elsevier DOI 2103
Contrast-weighted dictionary, Dictionary learning, Gradient optimization, Remote sensing, Saliency detection BibRef

Fan, J.L.[Jin-Long], Zhang, X.Y.[Xiao-Yu], Zhao, C.L.[Chun-Liang], Qin, Z.H.[Zhi-Hao], de Vroey, M.[Mathilde], Defourny, P.[Pierre],
Evaluation of Crop Type Classification with Different High Resolution Satellite Data Sources,
RS(13), No. 5, 2021, pp. xx-yy.
DOI Link 2103

Luo, X.[Xin], Du, H.Q.[Hua-Qiang], Zhou, G.[Guomo], Li, X.J.[Xue-Jian], Mao, F.J.[Fang-Jie], Zhu, D.[Di'en], Xu, Y.X.[Yan-Xin], Zhang, M.[Meng], He, S.B.[Shao-Bai], Huang, Z.H.[Zi-Hao],
A Novel Query Strategy-Based Rank Batch-Mode Active Learning Method for High-Resolution Remote Sensing Image Classification,
RS(13), No. 11, 2021, pp. xx-yy.
DOI Link 2106

Chen, Y.L.[Yu-Ling], Teng, W.T.[Wen-Tao], Li, Z.[Zhen], Zhu, Q.Q.[Qi-Qi], Guan, Q.F.[Qing-Feng],
Cross-Domain Scene Classification Based on a Spatial Generalized Neural Architecture Search for High Spatial Resolution Remote Sensing Images,
RS(13), No. 17, 2021, pp. xx-yy.
DOI Link 2109

Shi, H.[Hao], Fan, J.H.[Jia-He], Wang, Y.P.[Yu-Pei], Chen, L.[Liang],
Dual Attention Feature Fusion and Adaptive Context for Accurate Segmentation of Very High-Resolution Remote Sensing Images,
RS(13), No. 18, 2021, pp. xx-yy.
DOI Link 2109

Guo, Y.J.[Yong-Jie], Wang, F.[Feng], Xiang, Y.M.[Yu-Ming], You, H.J.[Hong-Jian],
DGFNet: Dual Gate Fusion Network for Land Cover Classification in Very High-Resolution Images,
RS(13), No. 18, 2021, pp. xx-yy.
DOI Link 2109

Lin, F.C.[Feng-Cheng], Chuang, Y.C.[Yung-Chung],
Interoperability Study of Data Preprocessing for Deep Learning and High-Resolution Aerial Photographs for Forest and Vegetation Type Identification,
RS(13), No. 20, 2021, pp. xx-yy.
DOI Link 2110

Shi, G.[Guang], Zhang, J.S.[Jiang-She], Liu, J.[Junmin], Zhang, C.X.[Chun-Xia], Zhou, C.S.[Chang-Sheng], Yang, S.Y.[Shu-Yun],
Global Context-Augmented Objection Detection in VHR Optical Remote Sensing Images,
GeoRS(59), No. 12, December 2021, pp. 10604-10617.
Object detection, Remote sensing, Feature extraction, Geospatial analysis, Optical sensors, Optical imaging, Convolution, rotation invariant BibRef

Gong, Y.P.[Yi-Ping], Zhang, F.[Fan], Jia, X.Y.[Xiang-Yang], Mao, Z.[Zhu], Huang, X.F.[Xian-Feng], Li, D.R.[De-Ren],
Instance Segmentation in Very High Resolution Remote Sensing Imagery Based on Hard-to-Segment Instance Learning and Boundary Shape Analysis,
RS(14), No. 1, 2022, pp. xx-yy.
DOI Link 2201

Kwong, I.H.Y.[Ivan H. Y.], Wong, F.K.K.[Frankie K. K.], Fung, T.[Tung], Liu, E.K.Y.[Eric K. Y.], Lee, R.H.[Roger H.], Ng, T.P.T.[Terence P. T.],
A Multi-Stage Approach Combining Very High-Resolution Satellite Image, GIS Database and Post-Classification Modification Rules for Habitat Mapping in Hong Kong,
RS(14), No. 1, 2022, pp. xx-yy.
DOI Link 2201

Yao, H.T.[Hong-Tai], Wang, X.P.[Xian-Pei], Zhao, L.[Le], Tian, M.[Meng], Jian, Z.[Zini], Gong, L.[Li], Li, B.[Bowen],
An Object-Based Markov Random Field with Partition-Global Alternately Updated for Semantic Segmentation of High Spatial Resolution Remote Sensing Image,
RS(14), No. 1, 2022, pp. xx-yy.
DOI Link 2201

Akcay, O.[Ozgun], Kinaci, A.C.[Ahmet Cumhur], Avsar, E.O.[Emin Ozgur], Aydar, U.[Umut],
Semantic Segmentation of High-Resolution Airborne Images with Dual-Stream DeepLabV3+,
IJGI(11), No. 1, 2022, pp. xx-yy.
DOI Link 2201

Qin, R.J.[Rong-Jun], Liu, T.[Tao],
A Review of Landcover Classification with Very-High Resolution Remotely Sensed Optical Images: Analysis Unit, Model Scalability and Transferability,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link 2202

Bhatt, P.[Parth], Maclean, A.[Ann], Dickinson, Y.[Yvette], Kumar, C.[Chandan],
Fine-Scale Mapping of Natural Ecological Communities Using Machine Learning Approaches,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link 2202

Zheng, Y.[Yalan], Yang, M.Y.[Meng-Yuan], Wang, M.[Min], Qian, X.J.[Xiao-Jun], Yang, R.[Rui], Zhang, X.[Xin], Dong, W.[Wen],
Semi-Supervised Adversarial Semantic Segmentation Network Using Transformer and Multiscale Convolution for High-Resolution Remote Sensing Imagery,
RS(14), No. 8, 2022, pp. xx-yy.
DOI Link 2205

Luo, Y.C.[Yu-Chuan], Zhang, Z.[Zhao], Zhang, L.L.[Liang-Liang], Han, J.C.[Ji-Chong], Cao, J.[Juan], Zhang, J.[Jing],
Developing High-Resolution Crop Maps for Major Crops in the European Union Based on Transductive Transfer Learning and Limited Ground Data,
RS(14), No. 8, 2022, pp. xx-yy.
DOI Link 2205

Xu, Y.Z.[Yi-Zhe], Jiang, J.[Jie],
High-Resolution Boundary-Constrained and Context-Enhanced Network for Remote Sensing Image Segmentation,
RS(14), No. 8, 2022, pp. xx-yy.
DOI Link 2205

Chen, F.L.[Feng-Lei], Liu, H.J.[Hai-Jun], Zeng, Z.H.[Zhi-Hong], Zhou, X.C.[Xi-Chuan], Tan, X.H.[Xiao-Heng],
BES-Net: Boundary Enhancing Semantic Context Network for High-Resolution Image Semantic Segmentation,
RS(14), No. 7, 2022, pp. xx-yy.
DOI Link 2205

Chen, P.[Pan], Zhang, B.[Bing], Hong, D.F.[Dan-Feng], Chen, Z.C.[Zheng-Chao], Yang, X.[Xuan], Li, B.P.[Bai-Peng],
FCCDN: Feature constraint network for VHR image change detection,
PandRS(187), 2022, pp. 101-119.
Elsevier DOI 2205
Change detection, Deep learning, Feature constraint BibRef

Chen, P.[Pan], Li, C.[Cong], Zhang, B.[Bing], Chen, Z.C.[Zheng-Chao], Yang, X.[Xuan], Lu, K.X.[Kai-Xuan], Zhuang, L.[Lina],
A Region-Based Feature Fusion Network for VHR Image Change Detection,
RS(14), No. 21, 2022, pp. xx-yy.
DOI Link 2212

Diao, C.Y.[Chun-Yuan], Li, G.[Geyang],
Near-Surface and High-Resolution Satellite Time Series for Detecting Crop Phenology,
RS(14), No. 9, 2022, pp. xx-yy.
DOI Link 2205

Li, Z.Q.[Zhu-Qiang], Chen, S.B.[Sheng-Bo], Meng, X.Y.[Xiang-Yu], Zhu, R.F.[Rui-Fei], Lu, J.Y.[Jun-Yan], Cao, L.[Lisai], Lu, P.[Peng],
Full Convolution Neural Network Combined with Contextual Feature Representation for Cropland Extraction from High-Resolution Remote Sensing Images,
RS(14), No. 9, 2022, pp. xx-yy.
DOI Link 2205

Yuan, J.[Jianye], Ma, X.[Xin], Han, G.[Ge], Li, S.[Song], Gong, W.[Wei],
Research on Lightweight Disaster Classification Based on High-Resolution Remote Sensing Images,
RS(14), No. 11, 2022, pp. xx-yy.
DOI Link 2206

Sun, C.Z.[Cheng-Zhe], Wu, J.J.[Jiang-Jiang], Chen, H.[Hao], Du, C.[Chun],
SemiSANet: A Semi-Supervised High-Resolution Remote Sensing Image Change Detection Model Using Siamese Networks with Graph Attention,
RS(14), No. 12, 2022, pp. xx-yy.
DOI Link 2206

Sharma, R.C.[Ram C.],
An Ultra-Resolution Features Extraction Suite for Community-Level Vegetation Differentiation and Mapping at a Sub-Meter Resolution,
RS(14), No. 13, 2022, pp. xx-yy.
DOI Link 2208

Ge, C.T.[Chu-Ting], Ding, H.Y.[Hai-Yong], Molina, I.[Inigo], He, Y.J.[Yong-Jian], Peng, D.F.[Dai-Feng],
Object-Oriented Change Detection Method Based on Spectral-Spatial-Saliency Change Information and Fuzzy Integral Decision Fusion for HR Remote Sensing Images,
RS(14), No. 14, 2022, pp. xx-yy.
DOI Link 2208

Wang, C.Y.[Chun-Yan], Wang, X.[Xiang], Wu, D.F.[Dan-Feng], Kuang, M.[Minchi], Li, Z.[Zhengtong],
Meticulous Land Cover Classification of High-Resolution Images Based on Interval Type-2 Fuzzy Neural Network with Gaussian Regression Model,
RS(14), No. 15, 2022, pp. xx-yy.
DOI Link 2208

Luo, M.[Muying], Ji, S.[Shunping],
Cross-spatiotemporal land-cover classification from VHR remote sensing images with deep learning based domain adaptation,
PandRS(191), 2022, pp. 105-128.
Elsevier DOI 2208
Domain adaptation, Land cover classification, Very-high resolution images, Deep learning, Cross-spatiotemporal BibRef

Boulila, W.[Wadii], Khlifi, M.K.[Manel Khazri], Ammar, A.[Adel], Koubaa, A.[Anis], Benjdira, B.[Bilel], Farah, I.R.[Imed Riadh],
A Hybrid Privacy-Preserving Deep Learning Approach for Object Classification in Very High-Resolution Satellite Images,
RS(14), No. 18, 2022, pp. xx-yy.
DOI Link 2209

Li, Z.H.[Zhuo-Hong], Zhang, H.Y.[Hong-Yan], Lu, F.X.[Fang-Xiao], Xue, R.[Ruoyao], Yang, G.Y.[Guang-Yi], Zhang, L.P.[Liang-Pei],
Breaking the resolution barrier: A low-to-high network for large-scale high-resolution land-cover mapping using low-resolution labels,
PandRS(192), 2022, pp. 244-267.
Elsevier DOI 2209
Multi-resolution, Land-cover mapping, Semantic segmentation, Low-to-high task BibRef

Sertel, E.[Elif], Ekim, B.[Burak], Osgouei, P.E.[Paria Ettehadi], Kabadayi, M.E.[M. Erdem],
Land Use and Land Cover Mapping Using Deep Learning Based Segmentation Approaches and VHR Worldview-3 Images,
RS(14), No. 18, 2022, pp. xx-yy.
DOI Link 2209

Li, J.[Jia], Liao, Y.J.[Yu-Jia], Zhang, J.J.[Jun-Jie], Zeng, D.[Dan], Qian, X.L.[Xiao-Liang],
Semi-Supervised DEGAN for Optical High-Resolution Remote Sensing Image Scene Classification,
RS(14), No. 17, 2022, pp. xx-yy.
DOI Link 2209

Tian, S.Q.[Shi-Qi], Zhong, Y.F.[Yan-Fei], Zheng, Z.[Zhuo], Ma, A.[Ailong], Tan, X.[Xicheng], Zhang, L.P.[Liang-Pei],
Large-Scale Deep Learning Based Binary and Semantic Change Detection in Ultra High Resolution Remote Sensing Imagery: From Benchmark Datasets to Urban Application,
PandRS(193), 2022, pp. 164-186.
Elsevier DOI 2210
Ultra high resolution, Semantic change detection, Deep learning, Remote sensing BibRef

Wang, X.[Xuan], Zhang, Y.[Yue], Lei, T.[Tao], Wang, Y.B.[Ying-Bo], Zhai, Y.J.[Yu-Jie], Nandi, A.K.[Asoke K.],
Dynamic Convolution Self-Attention Network for Land-Cover Classification in VHR Remote-Sensing Images,
RS(14), No. 19, 2022, pp. xx-yy.
DOI Link 2210

Wu, C.[Chen], Chen, H.[Hongruixuan], Du, B.[Bo], Zhang, L.P.[Liang-Pei],
Unsupervised Change Detection in Multitemporal VHR Images Based on Deep Kernel PCA Convolutional Mapping Network,
Cyber(52), No. 11, November 2022, pp. 12084-12098.
Feature extraction, Principal component analysis, Kernel, Convolution, Remote sensing, Training, Task analysis, very-high-resolution (VHR) images BibRef

Jiang, Z.R.[Zhuo-Ran], Zhou, X.X.[Xin-Xin], Cao, W.[Wei], Sun, Z.H.[Zai-Hong], Wu, C.B.[Chang-Bin],
ICD: VHR-Oriented Interactive Change-Detection Algorithm,
IJGI(11), No. 10, 2022, pp. xx-yy.
DOI Link 2211

Ling, J.[Jie], Hu, L.[Lei], Cheng, L.[Lang], Chen, M.H.[Ming-Hui], Yang, X.[Xin],
IRA-MRSNet: A Network Model for Change Detection in High-Resolution Remote Sensing Images,
RS(14), No. 21, 2022, pp. xx-yy.
DOI Link 2212

Chaib, S.[Souleyman], Mansouri, D.E.[Dou El_Kefel], Omara, I.[Ibrahim], Hagag, A.[Ahmed], Dhelim, S.[Sahraoui], Bensaber, D.A.[Djamel Amar],
On the Co-Selection of Vision Transformer Features and Images for Very High-Resolution Image Scene Classification,
RS(14), No. 22, 2022, pp. xx-yy.
DOI Link 2212

Luo, Y.Y.[Yi-Yun], Wang, J.N.[Jin-Nian], Yang, X.K.[Xian-Kun], Yu, Z.Y.[Zhen-Yu], Tan, Z.X.[Zi-Xuan],
Pixel Representation Augmented through Cross-Attention for High-Resolution Remote Sensing Imagery Segmentation,
RS(14), No. 21, 2022, pp. xx-yy.
DOI Link 2212

Mallet, C., Le Bris, A.,
Current Challenges In Operational Very High Resolution Land-cover Mapping,
DOI Link 2012

Pluto-Kossakowska, J.,
Automatic Detection of Grey Infrastructure Based on VHR Image,
DOI Link 2012

James, D., Collin, A., Mury, A., Costa, S.,
Very High Resolution Land Use and Land Cover Mapping Using Pleiades-1 Stereo Imagery and Machine Learning,
DOI Link 2012

Bousias Alexakis, E., Armenakis, C.,
Evaluation of Semi-supervised Learning for CNN-based Change Detection,
ISPRS21(B3-2021: 829-836).
DOI Link 2201
Evaluation of Unet and Unet++ Architectures In High Resolution Image Change Detection Applications,
DOI Link 2012

Li, L.W.[Long-Wei], Xi, J.B.[Jiang-Bo], Jiang, W.D.[Wan-Dong], Cong, M.[Ming], Han, L.[Ling], Yang, Y.[Yun],
Multi-scale Fast Detection of Objects in High Resolution Remote Sensing Images,
Remote sensing, Feature extraction, Image resolution, Object detection, Machine learning, Data models, YOLOv3 BibRef

Robinson, C.[Caleb], Hou, L.[Le], Malkin, K.[Kolya], Soobitsky, R.[Rachel], Czawlytko, J.[Jacob], Dilkina, B.[Bistra], Jojic, N.[Nebojsa],
Large Scale High-Resolution Land Cover Mapping With Multi-Resolution Data,

Xu, Y., Hu, X., Wei, Y., Yang, Y., Wang, D.,
A Machine Learning Dataset for Large-scope High Resolution Remote Sensing Image Interpretation Considering Landscape Spatial Heterogeneity,
DOI Link 1912

Chen, Y., Ming, D.,
Superpixel Classification of High Spatial Resolution Remote Sensing Image Based On Multi-Scale CNN and Scale Parameter Estimation,
DOI Link 1912

Wu, L.[Linmei], Shen, L.[Li], Li, Z.P.[Zhi-Peng],
A Kernel Method Based On Topic Model For Very High Spatial Resolution (VHSR) Remote Sensing Image Classification,
ISPRS16(B7: 399-403).
DOI Link 1610

Fan, J., Chen, T., Lu, S.,
Vegetation coverage detection from very high resolution satellite imagery,
Histograms BibRef

Taberner, M., Shutler, J., Walker, P., Poulter, D., Piolle, J.F., Donlon, C., Guidetti, V.,
The ESA FELYX High Resolution Diagnostic Data Set System Design and Implementation,
DOI Link 1402

Bindel, M., Hese, S., Berger, C., Schmullius, C.,
Feature selection from high resolution remote sensing data for biotope mapping,
PDF File. 1106

Arroyo, L.A.[Lara A.], Johansen, K.[Kasper], Phinn, S.R.[Stuart R.],
Mapping Land Cover Types from Very High Spatial Resolution Imagery: Automatic Application of an Object Based Classification Scheme,
PDF File. 1007

Carleer, A.P., Wolff, E.,
Region-based classification potential for land-cover classification with very high spatial resolution satellite data,
PDF File. 0607

Agrafiotis, P., Georgopoulos, A.,
Comparative Assessment of Very High Resolution Satellite and Aerial Orthoimagery,
DOI Link 1504

Aminipouri, M., Sliuzas, R., Kuffer, M.,
Object-Oriented Analysis of Very High Resolution Orthophotos for Estimating the Population of Slum Areas, A Case of Dar-Es-Salaam, Tanzania,
PDF File. 0906

Zhang, J.Q.[Jian-Qing], Zhang, Z.X.[Zu-Xun],
Strict Geometric Model Based on Affine Transformation for Remote Sensing Image with High Resolution,
PCV02(B: 309). 0305

Chapter on Remote Sensing General Issue, Land Use, Land Cover continues in
Object Based Land Cover, Region Based Land Cover, Land Use Analysis .

Last update:Dec 4, 2022 at 15:58:45