Journal
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
Volume 14, Issue 12, Pages 2418-2422Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2017.2766840
Keywords
Change detection (CD); convolutional neural network (CNN); low rank; remote sensing (RS); visual saliency
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Funding
- Natural Science Foundation of China [61601011, 61421003]
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In this letter, we address the problem of change detection for remote sensing images from the perspective of visual saliency computation. The proposed method incorporates low-rank-based saliency computation and deep feature representation. First, multilevel convolutional neural network (CNN) features are extracted for superpixels generated using SLIC, in which a fixed-size CNN feature can be formed to represent each superpixel. Then, low-rank decomposition is applied to the change features of the two input images to generate saliency maps that indicate change probabilities of each pixel. Finally, binarized change map can be obtained with a simple threshold. To deal with scale variations, a multiscale fusion strategy is employed to produce more reliable detection results. Extensive experiments on Google Earth and GF-2 images demonstrate the feasibility and effectiveness of the proposed method.
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