Subsidence feature discrimination using deep convolutional neural networks in synthetic aperture radar imagery

Abstract

Effective detection and discrimination of surface deformation features in Synthetic Aperture Radar imagery is one of the most important applications of the data. Areas that undergo surface deformation can pose health and safety risks which necessitates an automatic and reliable means of surface deformation discrimination. Due to the similarities between… (More)
DOI: 10.1109/IGARSS.2017.8128031

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