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
BibRef
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.
IEEE DOI
1402
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
BibRef
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
BibRef
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
BibRef
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
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
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,
ICPR16(3566-3571)
IEEE DOI
1705
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,
ICIP10(1045-1048).
IEEE DOI
1009
BibRef
Fan, J.,
Chen, T.,
Lu, S.,
Vegetation coverage detection from very high resolution satellite
imagery,
VCIP15(1-4)
IEEE DOI
1605
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,
GEOBIA10(xx-yy).
PDF File.
1007
BibRef
Carleer, A.P.,
Wolff, E.,
Region-based classification potential for land-cover classification
with very high spatial resolution satellite data,
OBIA06(xx-yy).
PDF File.
0607
BibRef
Chapter on Remote Sensing, Cartography, Aerial Images, Buildings, Roads, Terrain, ATR continues in
Object Based Land Cover, Region Based Land Cover, Land Use Analysis .