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

Chapter Contents (Back)
High Resolution. VHR, HR.

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.[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

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

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

Wang, M.[Min], Cui, Q.[Qi], Sun, Y.[Yujie], Wang, Q.[Qiao],
Photovoltaic panel extraction from very high-resolution aerial imagery using region-line primitive association analysis and template matching,
PandRS(141), 2018, pp. 100-111.
Elsevier DOI 1806
Photovoltaic panel, Object-based image analysis, Region-line primitive association framework, High-resolution imagery BibRef

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

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

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,

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

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., Foody, G.M., Li, X.D.[Xiao-Dong], Zhang, X., Fang, S., 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.[Lihui], 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], Ge, Y.[Yong], Chen, Y.[Yu], Jin, Y.[Yan], An, R.[Ru],
Subpixel Land Cover Mapping Using Multiscale Spatial Dependence,
GeoRS(56), No. 9, September 2018, pp. 5097-5106.
Remote sensing, Optimization, Spatial resolution, Image segmentation, Feature extraction, Graphical models, subpixel mapping (SPM) 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.[Ruixi], Yan, L.[Li], Mo, N.[Nan], Liu, Y.[Yi],
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

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

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, Cartography, Aerial Images, Buildings, Roads, Terrain, ATR continues in
Object Based Land Cover, Region Based Land Cover, Land Use Analysis .

Last update:Oct 1, 2019 at 15:23:24