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26 November 2019 High-resolution optical remote sensing imagery change detection through deep transfer learning
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Change detection is a challenging task that has received much attention in the remote sensing field. Whereas numerous remote sensing change detection methods have been developed, the efficiency of these approaches is insufficient to meet the real-world applications’ requirements. Recently, deep learning methods have been largely used for remote sensing imagery change detection, most of these approaches are limited by their training dataset. However, adapting a pretrained convolutional neural network (CNN) on an image classification task to change detection is extremely challenging. An automatic land cover/use change detection approach based on fast and accurate frameworks for optical high-resolution remote sensing imagery is proposed. The fast framework is designed for applications that require immediate results with less complexity. The accurate framework is designed for applications that require high levels of precision, it decomposes large images into small processing blocks and forwards them into CNN. The proposed frameworks can learn transferable features from one task to another and escape the use of the expensive and inaccurate handcrafted features and the requirements of the big training dataset. A number of experiments were carried out to validate the proposed approach on three real bitemporal images. The experimental results illustrate the superiority of the proposed approach over other state-of-the-art methods.

© 2019 Society of Photo-Optical Instrumentation Engineers (SPIE) 1931-3195/2019/$28.00 © 2019 SPIE
Mohammed El Amin Larabi, Souleyman Chaib, Bakhti Khadidja, Kamel Hasni, and Mohammed Amine Bouhlala "High-resolution optical remote sensing imagery change detection through deep transfer learning," Journal of Applied Remote Sensing 13(4), 046512 (26 November 2019).
Received: 20 June 2019; Accepted: 8 November 2019; Published: 26 November 2019

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