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IEEE DOI
1402
decision trees
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An Object-Based Image Analysis Approach for Detecting Penguin Guano
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BibRef
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For very high resolution, use regions (objects).
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Semantic labeling, Deep learning, Rotation invariance, Sub-decimeter resolution
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CVPR18(8877-8885)
IEEE DOI
1812
Buildings, Image segmentation, Active contours, Force, Training,
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Semantic labeling, Convolutional neural networks (CNNs),
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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,
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Feature Learning, High-resolution Images, Land-cover Mapping,
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2106
Semantic segmentation, Deep learning,
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Convolutional neural network
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1901
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Flores, E.[Eliezer],
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Elsevier DOI
1902
Deep learning, Dictionary learning, Feature learning,
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Farmland Extraction from High Spatial Resolution Remote Sensing
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1902
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DOI Link
1902
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Mboga, N.[Nicholus],
Georganos, S.[Stefanos],
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Classification, Support Vector Machines, Self-learning,
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vegetation mapping
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IEEE DOI
1907
Spatial resolution, Remote sensing, Adaptation models,
Graphical models, Distribution functions, Forestry, Image series,
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geophysical image processing, image classification, land cover,
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Land cover
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Earth
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geophysical image processing
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2001
Feature extraction, Object detection, Proposals,
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Mapping Vegetation at Species Level with High-Resolution
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2003
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Change Detection in Multisource VHR Images via Deep Siamese
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IEEE DOI
2004
Change detection (CD),
deep siamese convolutional multiple-layers recurrent neural network,
very-high-resolution (VHR) images
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Zhang, X.G.[Xin-Gang],
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Samasse, K.[Kaboro],
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A High-Resolution Cropland Map for the West African Sahel Based on
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2005
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IEEE DOI
2007
Semantics, Training, Image segmentation, Feature extraction,
Computer architecture, Remote sensing, Convolution,
semantic segmentation
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Uncertainty Analysis for Object-Based Change Detection in Very
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CSVM Architectures for Pixel-Wise Object Detection in High-Resolution
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GeoRS(58), No. 9, September 2020, pp. 6059-6070.
IEEE DOI
2008
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.
IEEE DOI
2011
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
BibRef
Qian, Y.[Yuguo],
Zhou, W.[Weiqi],
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
BibRef
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.
IEEE DOI
2012
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
BibRef
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
BibRef
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
BibRef
Chen, Y.L.[Yu-Ling],
Teng, W.[Wentao],
Li, Z.[Zhen],
Zhu, Q.[Qiqi],
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
BibRef
Shi, H.[Hao],
Fan, J.[Jiahe],
Wang, Y.[Yupei],
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
BibRef
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
BibRef
Pluto-Kossakowska, J.,
Automatic Detection of Grey Infrastructure Based on VHR Image,
ISPRS20(B3:181-187).
DOI Link
2012
BibRef
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,
ISPRS20(B2:675-682).
DOI Link
2012
BibRef
Bousias Alexakis, E.,
Armenakis, C.,
Evaluation of Unet and Unet++ Architectures In High Resolution Image
Change Detection Applications,
ISPRS20(B3:1507-1514).
DOI Link
2012
BibRef
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,
ICIVC20(5-10)
IEEE DOI
2009
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,
CVPR19(12718-12727).
IEEE DOI
2002
BibRef
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,
IWIDF19(731-736).
DOI Link
1912
BibRef
Chen, Y.,
Ming, D.,
Superpixel Classification of High Spatial Resolution Remote Sensing
Image Based On Multi-Scale CNN and Scale Parameter Estimation,
IWIDF19(681-685).
DOI Link
1912
BibRef
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
BibRef
Fan, J.,
Chen, T.,
Lu, S.,
Vegetation coverage detection from very high resolution satellite
imagery,
VCIP15(1-4)
IEEE DOI
1605
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,
SSG13(243-249).
DOI Link
1402
BibRef
Bindel, M.,
Hese, S.,
Berger, C.,
Schmullius, C.,
Feature selection from high resolution remote sensing data for biotope
mapping,
HighRes11(xx-yy).
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1106
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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
Agrafiotis, P.,
Georgopoulos, A.,
Comparative Assessment of Very High Resolution Satellite and Aerial
Orthoimagery,
PIA15(1-7).
DOI Link
1504
BibRef
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,
HighRes09(xx-yy).
PDF File.
0906
BibRef
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
BibRef
Chapter on Remote Sensing General Issue, Land Use, Land Cover continues in
Object Based Land Cover, Region Based Land Cover, Land Use Analysis .