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An Automated Framework for Change Detection with Contextual Understanding Using Remote Sensing Data

As Remote Sensing technologies undergoes accelerated evolution, excessive amount of satellite imagery collected by both active and passive sensors becomes accessible, from which the observation and assessment of the Earth's environment are predominantly advantaged. This paper proposes an automated two-stage framework, employing differencing, clustering and thresholding techniques on images captured by Synthetic Aperture Radar (SAR) for change detection, and Deep Neural Network (DNN) for scene classification on optical imagery. With target region's location and a timespan as input, the recommended scheme is capable of delivering geographical description about the potential changes occurred within the area and given interval, as well as a semantic understanding about the region's context. Experimental results and quantitative assessment on three real datasets are incorporated to verify the effectiveness of the proposed work, while its deficiencies and future research potentials are deliberately discussed as well.