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IEEE DOI
1402
decision trees
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Witharana, C.[Chandi],
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BibRef
Georganos, S.[Stefanos],
Grippa, T.[Tais],
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Scale Matters: Spatially Partitioned Unsupervised Segmentation
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For very high resolution, use regions (objects).
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Elsevier DOI
1810
Semantic labeling, Deep learning, Rotation invariance, Sub-decimeter resolution
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Zhang, L.,
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Marcos, D.,
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Learning Deep Structured Active Contours End-to-End,
CVPR18(8877-8885)
IEEE DOI
1812
Buildings, Image segmentation, Active contours, Force, Training,
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Elsevier DOI
1810
Semantic labeling, Convolutional neural networks (CNNs),
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Elsevier DOI
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Cross-modality, Graph learning, Hyperspectral,
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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
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Luo, B.[Bin],
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IEEE DOI
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DOI Link
1901
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Real-Time Dense Semantic Labeling with Dual-Path Framework for
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RS(11), No. 24, 2019, pp. xx-yy.
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1912
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Flores, E.[Eliezer],
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Elsevier DOI
1902
Deep learning, Dictionary learning, Feature learning,
Land-use classification, Sparse representation
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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.
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1902
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Mo, N.[Nan],
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Xie, H.[Hong],
Class-Specific Anchor Based and Context-Guided Multi-Class Object
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RS(11), No. 3, 2019, pp. xx-yy.
DOI Link
1902
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Georganos, S.[Stefanos],
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1903
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Elsevier DOI
1904
Classification, Support Vector Machines, Self-learning,
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Zhu, J.L.[Jiang-Ling],
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1701
vegetation mapping
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1606
Algorithm design and analysis
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IEEE DOI
1907
Spatial resolution, Remote sensing, Adaptation models,
Graphical models, Distribution functions, Forestry, Image series,
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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.
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IEEE DOI
1801
geophysical image processing, image classification, land cover,
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Li, X.D.[Xiao-Dong],
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1407
Land cover
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Li, X.D.[Xiao-Dong],
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IEEE DOI
1606
Earth
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Ling, F.,
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Ge, Y.,
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1612
geophysical image processing
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Superresolution Land Cover Mapping With Multiscale Information by
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1407
land cover
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1909
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1909
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Zhu, R.X.[Rui-Xi],
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Elsevier DOI
1909
Aerial imagery, Semantic segmentation, Tree structures,
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1910
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1911
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Li, Y.S.[Yan-Shan],
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1911
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Tong, H.J.[Heng-Jian],
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Purifying SLIC Superpixels to Optimize Superpixel-Based
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1911
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Wu, Y.D.[Ying-Dan],
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Li, E.[Erzhu],
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Zhang, S.Y.[Shu-Yu],
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Wu, T.J.[Tian-Jun],
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Gong, Y.,
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IEEE DOI
2001
Feature extraction, Object detection, Proposals,
Semantics, Context modeling, Convolutional codes,
object detection
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Zhang, X.[Xin],
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How Well Do Deep Learning-Based Methods for Land Cover Classification
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2002
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Liang, W.W.[Wan-Wan],
Abidi, M.[Mongi],
Carrasco, L.[Luis],
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Mapping Vegetation at Species Level with High-Resolution
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2003
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Lin, W.J.[Wen-Jie],
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Parallel Regional Segmentation Method of High-Resolution Remote
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2003
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An End-to-End and Localized Post-Processing Method for Correcting
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2003
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Meier, E.S.[Eliane Seraina],
Indermaur, A.[Alexander],
Ginzler, C.[Christian],
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An Effective Way to Map Land-Use Intensity with a High Spatial
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2003
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Chen, H.R.[Hong-Ruixuan],
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Du, B.[Bo],
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Wang, L.[Le],
Change Detection in Multisource VHR Images via Deep Siamese
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GeoRS(58), No. 4, April 2020, pp. 2848-2864.
IEEE DOI
2004
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],
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High-Resolution Remote Sensing Image Integrity Authentication Method
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2005
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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
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2005
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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
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2006
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Körez, A.[Atakan],
Barisçi, N.[Necaattin],
Çetin, A.[Aydin],
Ergün, U.[Uçman],
Weighted Ensemble Object Detection with Optimized Coefficients for
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2006
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Deigele, W.[Wolfgang],
Brandmeier, M.[Melanie],
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A Hierarchical Deep-Learning Approach for Rapid Windthrow Detection
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2007
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Ding, L.,
Zhang, J.,
Bruzzone, L.,
Semantic Segmentation of Large-Size VHR Remote Sensing Images Using a
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GeoRS(58), No. 8, August 2020, pp. 5367-5376.
IEEE DOI
2007
Semantics, Training, Image segmentation, Feature extraction,
Computer architecture, 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
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2008
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Li, Y.,
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He, B.,
CSVM Architectures for Pixel-Wise Object Detection in High-Resolution
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IEEE DOI
2008
Object detection, Convolution, Remote sensing, Feature extraction,
Training, Image resolution, Support vector machines,
very high resolution (VHR)
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Watt, M.S.[Michael S.],
Buddenbaum, H.[Henning],
Leonardo, E.M.C.[Ellen Mae C.],
Estarija, H.J.C.[Honey Jane C.],
Bown, H.E.[Horacio E.],
Gomez-Gallego, M.[Mireia],
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Massam, P.[Peter],
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Zarco-Tejada, P.J.[Pablo J.],
Using hyperspectral plant traits linked to photosynthetic efficiency
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Elsevier DOI
2011
High resolution hyperspectral, N:P ratio, Nitrogen,
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Chapter on Remote Sensing, Cartography, Aerial Images, Buildings, Roads, Terrain, ATR continues in
Object Based Land Cover, Region Based Land Cover, Land Use Analysis .