High-resolution remote-sensing imagery retrieval using sparse features by auto-encoder

Remote Sensing Letters

Volumn 6, Issue 10, 2015, Pages 775-783

  Zhou, Weixun ^a,b   Shao, Zhenfeng ^a,b   Diao, Chunyuan ^c   Cheng, Qimin ^d  

^a WUHAN UNIVERSITY   (China)

^b Collaborative Innovation Centre for Geospatial Technology ^*  (China)

^c UNIVERSITY AT BUFFALO   (United States)

^d HUAZHONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

LEARNING SYSTEMS;

EFFECTIVE APPROACHES; FEATURE REPRESENTATION; HIGH RESOLUTION REMOTE SENSING IMAGERY; REMOTE SENSING DATA; REMOTE SENSING IMAGERY; STATE-OF-THE-ART METHODS; UNIVERSITY OF CALIFORNIA; UNSUPERVISED FEATURE LEARNING;

REMOTE SENSING;

ARTIFICIAL NEURAL NETWORK; DATA SET; IMAGE ANALYSIS; LEARNING; PERFORMANCE ASSESSMENT; REMOTE SENSING; SPATIAL RESOLUTION;

EID: 84940766118     PISSN: 2150704X     EISSN: 21507058     Source Type: Journal    
DOI: 10.1080/2150704X.2015.1074756     Document Type: Article

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