A Machine Learning Framework for Detecting Landslides on Earthen Levees Using Spaceborne SAR Imagery

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

Volumn 8, Issue 8, 2015, Pages 3791-3801

  Mahrooghy, Majid ^a   Aanstoos, James V ^b   Nobrega, Rodrigo A A ^c   Hasan, Khaled ^b   Prasad, Saurabh ^d   Younan, Nicolas H ^e  

^a UNIVERSITY OF PENNSYLVANIA   (United States)

^b MISSISSIPPI STATE UNIVERSITY   (United States)

^c FEDERAL UNIVERSITY OF MINAS GERAIS   (Brazil)

^d UNIVERSITY OF HOUSTON   (United States)

^e MISSISSIPPI STATE UNIVERSITY   (United States)

Author keywords

Feature extraction; feature selection; hazards; support vector machine (SVM); synthetic aperture radar (SAR)

Indexed keywords

ARTIFICIAL INTELLIGENCE; BACKPROPAGATION; EMBANKMENTS; EXTRACTION; FEATURE EXTRACTION; HAZARDS; LEARNING SYSTEMS; LEVEES; MAXIMUM LIKELIHOOD; NEURAL NETWORKS; RADAR; RADAR IMAGING; SUPPORT VECTOR MACHINES;

BACK-PROPAGATION NEURAL NETWORKS; CLASSIFICATION ACCURACY; DIGITAL ELEVATION MAP; FEATURE SELECTION METHODS; GROUND TRUTH DATA; MINIMUM REDUNDANCY-MAXIMUM RELEVANCES; RADIOMETRIC NORMALIZATION; X-BAND SYNTHETIC APERTURE RADARS;

SYNTHETIC APERTURE RADAR;

ARTIFICIAL NEURAL NETWORK; BACK PROPAGATION; DIGITAL ELEVATION MODEL; LANDSLIDE; LEVEE; MACHINE LEARNING; MAXIMUM LIKELIHOOD ANALYSIS; RADAR IMAGERY; SUPPORT VECTOR MACHINE; SYNTHETIC APERTURE RADAR;

MISSISSIPPI RIVER; UNITED STATES;

EID: 85027931447     PISSN: 19391404     EISSN: 21511535     Source Type: Journal    
DOI: 10.1109/JSTARS.2015.2427337     Document Type: Article

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