Land Cover, Land Use, Very High Resolution

Chapter Contents (Back)
High Resolution.

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,

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

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

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:Jan 16, 2019 at 14:27:27