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

Chapter Contents (Back)
High Resolution. VHR, HR. See also Subpixel Target, Subpixel Land Use, Tiny Objects.

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
BibRef

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

Shi, C.[Cheng], Lv, Z.Y.[Zhi-Yong], Yang, X.H.[Xiu-Hong], Xu, P.F.[Peng-Fei], Bibi, I.[Irfana],
Hierarchical Multi-View Semi-Supervised Learning for Very High-Resolution Remote Sensing Image Classification,
RS(12), No. 6, 2020, pp. xx-yy.
DOI Link 2003
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

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,
CVPR18(8877-8885)
IEEE DOI 1812
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,
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

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
BibRef

Wang, Y.[Yuhao], Chen, C.[Chen], Ding, M.[Meng], Li, J.Y.[Jiang-Yun],
Real-Time Dense Semantic Labeling with Dual-Path Framework for High-Resolution Remote Sensing Image,
RS(11), No. 24, 2019, pp. xx-yy.
DOI Link 1912
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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.
IEEE DOI 1701
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.
IEEE DOI 1606
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.
IEEE DOI 1907
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], 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.
IEEE DOI 1801
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
BibRef

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.
IEEE DOI 1606
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.
IEEE DOI 1612
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
BibRef

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.
IEEE DOI 1407
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
BibRef

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
BibRef

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
BibRef

Zhu, R.X.[Rui-Xi], Yan, L.[Li], Mo, N.[Nan], Liu, Y.[Yi],
Retraction: 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
BibRef
And: Retraction: RS(12), No. 4, 2020, pp. xx-yy.
DOI Link 2003
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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
BibRef

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
BibRef

Li, H.P.[Hua-Peng], Zhang, C.[Ce], Zhang, S.Q.[Shu-Qing], Atkinson, P.M.[Peter M.],
A hybrid OSVM-OCNN Method for Crop Classification from Fine Spatial Resolution Remotely Sensed Imagery,
RS(11), No. 20, 2019, pp. xx-yy.
DOI Link 1910
BibRef

Abdollahnejad, A.[Azadeh], Panagiotidis, D.[Dimitrios], Bílek, L.[Lukáš],
An Integrated GIS and Remote Sensing Approach for Monitoring Harvested Areas from Very High-Resolution, Low-Cost Satellite Images,
RS(11), No. 21, 2019, pp. xx-yy.
DOI Link 1911
BibRef

Li, Y.S.[Yan-Shan], Xu, J.J.[Jian-Jie], Xia, R.J.[Rong-Jie], Wang, X.C.[Xian-Chen], Xie, W.X.[Wei-Xin],
A two-stage framework of target detection in high-resolution hyperspectral images,
SIViP(13), No. 7, October 2019, pp. 1339-1346.
Springer DOI 1911
BibRef

Tong, H.J.[Heng-Jian], Tong, F.[Fei], Zhou, W.[Wei], Zhang, Y.[Yun],
Purifying SLIC Superpixels to Optimize Superpixel-Based Classification of High Spatial Resolution Remote Sensing Image,
RS(11), No. 22, 2019, pp. xx-yy.
DOI Link 1911
BibRef

Wu, Y.D.[Ying-Dan], Di, L.P.[Li-Ping], Ming, Y.[Yang], Lv, H.[Hui], Tan, H.[Han],
High-Resolution Optical Remote Sensing Image Registration via Reweighted Random Walk Based Hyper-Graph Matching,
RS(11), No. 23, 2019, pp. xx-yy.
DOI Link 1912
BibRef

Li, E.[Erzhu], Samat, A.[Alim], Liu, W.[Wei], Lin, C.[Cong], Bai, X.[Xuyu],
High-Resolution Imagery Classification Based on Different Levels of Information,
RS(11), No. 24, 2019, pp. xx-yy.
DOI Link 1912
BibRef

Zhang, G.[Gang], Lei, T.[Tao], Cui, Y.[Yi], Jiang, P.[Ping],
A Dual-Path and Lightweight Convolutional Neural Network for High-Resolution Aerial Image Segmentation,
IJGI(8), No. 12, 2019, pp. xx-yy.
DOI Link 1912
BibRef

Zhang, S.Y.[Shu-Yu], Li, C.[Chuanrong], Qiu, S.[Shi], Gao, C.[Caixia], Zhang, F.[Feng], Du, Z.H.[Zhen-Hong], Liu, R.[Renyi],
EMMCNN: An ETPS-Based Multi-Scale and Multi-Feature Method Using CNN for High Spatial Resolution Image Land-Cover Classification,
RS(12), No. 1, 2019, pp. xx-yy.
DOI Link 2001
BibRef

Wu, T.J.[Tian-Jun], Luo, J.C.[Jian-Cheng], Zhou, Y.[Ya'nan], Wang, C.P.[Chang-Peng], Xi, J.B.[Jiang-Bo], Fang, J.[Jianwu],
Geo-Object-Based Land Cover Map Update for High-Spatial-Resolution Remote Sensing Images via Change Detection and Label Transfer,
RS(12), No. 1, 2020, pp. xx-yy.
DOI Link 2001
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Gong, Y., Xiao, Z., Tan, X., Sui, H., Xu, C., Duan, H., Li, D.,
Context-Aware Convolutional Neural Network for Object Detection in VHR Remote Sensing Imagery,
GeoRS(58), No. 1, January 2020, pp. 34-44.
IEEE DOI 2001
Feature extraction, Object detection, Proposals, Semantics, Context modeling, Convolutional codes, object detection BibRef

Zhang, X.[Xin], Han, L.X.[Liang-Xiu], Han, L.H.[Liang-Hao], Zhu, L.[Liang],
How Well Do Deep Learning-Based Methods for Land Cover Classification and Object Detection Perform on High Resolution Remote Sensing Imagery?,
RS(12), No. 3, 2020, pp. xx-yy.
DOI Link 2002
BibRef

Liang, W.W.[Wan-Wan], Abidi, M.[Mongi], Carrasco, L.[Luis], McNelis, J.[Jack], Tran, L.[Liem], Li, Y.K.[Ying-Kui], Grant, J.[Jerome],
Mapping Vegetation at Species Level with High-Resolution Multispectral and Lidar Data Over a Large Spatial Area: A Case Study with Kudzu,
RS(12), No. 4, 2020, pp. xx-yy.
DOI Link 2003
BibRef

Lin, W.J.[Wen-Jie], Li, Y.[Yu],
Parallel Regional Segmentation Method of High-Resolution Remote Sensing Image Based on Minimum Spanning Tree,
RS(12), No. 5, 2020, pp. xx-yy.
DOI Link 2003
BibRef

Pan, X.[Xin], Zhao, J.[Jian], Xu, J.[Jun],
An End-to-End and Localized Post-Processing Method for Correcting High-Resolution Remote Sensing Classification Result Images,
RS(12), No. 5, 2020, pp. xx-yy.
DOI Link 2003
BibRef

Meier, E.S.[Eliane Seraina], Indermaur, A.[Alexander], Ginzler, C.[Christian], Psomas, A.[Achilleas],
An Effective Way to Map Land-Use Intensity with a High Spatial Resolution Based on Habitat Type and Environmental Data,
RS(12), No. 6, 2020, pp. xx-yy.
DOI Link 2003
BibRef

Chen, H.R.[Hong-Ruixuan], Wu, C.[Chen], Du, B.[Bo], Zhang, L.P.[Liang-Pei], Wang, L.[Le],
Change Detection in Multisource VHR Images via Deep Siamese Convolutional Multiple-Layers Recurrent Neural Network,
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], Wang, H.[Hao],
High-Resolution Remote Sensing Image Integrity Authentication Method Considering Both Global and Local Features,
IJGI(9), No. 4, 2020, pp. xx-yy.
DOI Link 2005
BibRef

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 Optimized Machine Learning,
RS(12), No. 9, 2020, pp. xx-yy.
DOI Link 2005
BibRef

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 Scene Classification of a High-Resolution Remote Sensing Image,
RS(12), No. 11, 2020, pp. xx-yy.
DOI Link 2006
BibRef

Körez, A.[Atakan], Barisçi, N.[Necaattin], Çetin, A.[Aydin], Ergün, U.[Uçman],
Weighted Ensemble Object Detection with Optimized Coefficients for Remote Sensing Images,
IJGI(9), No. 6, 2020, pp. xx-yy.
DOI Link 2006
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Deigele, W.[Wolfgang], Brandmeier, M.[Melanie], Straub, C.[Christoph],
A Hierarchical Deep-Learning Approach for Rapid Windthrow Detection on PlanetScope and High-Resolution Aerial Image Data,
RS(12), No. 13, 2020, pp. xx-yy.
DOI Link 2007
BibRef

Ding, L., Zhang, J., Bruzzone, L.,
Semantic Segmentation of Large-Size VHR Remote Sensing Images Using a Two-Stage Multiscale Training Architecture,
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 High-Resolution Satellite Images Using Deep Learning Network,
RS(12), No. 15, 2020, pp. xx-yy.
DOI Link 2008
BibRef

Li, Y., Melgani, F.[Farid], He, B.,
CSVM Architectures for Pixel-Wise Object Detection in High-Resolution Remote Sensing Images,
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

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], Hartley, R.[Robin], Massam, P.[Peter], Wright, L.[Liam], Zarco-Tejada, P.J.[Pablo J.],
Using hyperspectral plant traits linked to photosynthetic efficiency to assess N and P partition,
PandRS(169), 2020, pp. 406-420.
Elsevier DOI 2011
High resolution hyperspectral, N:P ratio, Nitrogen, Nutrient limitation, Phosphorus, Reflectance 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, 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).
PDF File. 1106
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

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


Last update:Nov 23, 2020 at 10:27:11