Automatic Change Detection in High-Resolution Remote Sensing Images by Using a Multiple Classifier System and Spectral-Spatial Features

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Volumn 9, Issue 8, 2016, Pages 3439-3451

  Tan, Kun ^a   Jin, Xiao ^a   Plaza, Antonio ^b   Wang, Xuesong ^a   Xiao, Liang ^c   Du, Peijun ^d  

^a CHINA UNIVERSITY OF MINING AND TECHNOLOGY   (China)

^b UNIVERSITY OF EXTREMADURA   (Spain)

^c NANJING UNIVERSITY   (China)

^d NANJING UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

Author keywords

Change detection (CD); extreme learning machines (ELMs); multiple classifier; spatial information

Indexed keywords

DEFORESTATION; IMAGE RECONSTRUCTION; LEARNING SYSTEMS; NEAREST NEIGHBOR SEARCH; OBJECT RECOGNITION; SAMPLING; SIGNAL DETECTION;

AUTOMATIC CHANGE DETECTION; EXTREME LEARNING MACHINE; HIGH RESOLUTION REMOTE SENSING IMAGES; K-NEAREST NEIGHBOR CLASSIFIERS (KNN); MORPHOLOGICAL PROFILE; MULTINOMIAL LOGISTIC REGRESSION; MULTIPLE CLASSIFIER SYSTEMS; REMOTE SENSING APPLICATIONS;

REMOTE SENSING;

ACCURACY ASSESSMENT; COMPARATIVE STUDY; DETECTION METHOD; IMAGE CLASSIFICATION; MULTISPECTRAL IMAGE; REMOTE SENSING; SPATIAL ANALYSIS; SPECTRAL ANALYSIS;

ULMUS;

EID: 84963627003     PISSN: 19391404     EISSN: 21511535     Source Type: Journal    
DOI: 10.1109/JSTARS.2016.2541678     Document Type: Article

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