Classification of remote sensed images using random forests and deep learning framework

Proceedings of SPIE - The International Society for Optical Engineering

Volumn 10004, Issue , 2016, Pages

  Piramanayagam, S ^a   Schwartzkopf, W ^b   Koehler, F W ^b   Saber, E ^a  

^a ROCHESTER INSTITUTE OF TECHNOLOGY   (United States)

^b National Geospatial Intelligence Agency   (United States)

Author keywords

Deep Learning; Random Forest for structured label

Indexed keywords

CLASSIFICATION (OF INFORMATION); CONVOLUTION; DECISION TREES; DEEP LEARNING; IMAGE PROCESSING; LEARNING ALGORITHMS; LEARNING SYSTEMS; NEURAL NETWORKS; PIXELS; REMOTE SENSING; SIGNAL PROCESSING; STATISTICAL TESTS; SYNTHETIC APERTURE RADAR;

CLASSIFICATION ACCURACY; CLASSIFICATION PERFORMANCE; CONVOLUTIONAL NEURAL NETWORK; DIGITAL SURFACE MODELS; LAND COVER CLASSIFICATION; RANDOM FOREST ALGORITHM; RANDOM FORESTS; STRUCTURAL INFORMATION;

IMAGE CLASSIFICATION;

EID: 85011339638     PISSN: 0277786X     EISSN: 1996756X     Source Type: Conference Proceeding    
DOI: 10.1117/12.2243169     Document Type: Conference Paper

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