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
BibRef
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.[Luká],
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
BibRef
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
BibRef
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
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 .