Journal
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
Volume 16, Issue 3, Pages 402-406Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2018.2876616
Keywords
Change detection; PCANet; saliency detection; synthetic aperture radar (SAR) image
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Funding
- Natural Science Foundation of China [61772390, 61871312]
- Aeronautical Science Foundation of China [2016081011]
- Natural Science Basic Research Plan in Shaanxi Province of China [2017JM4022]
- Shanghai Aerospace Science and Technology Innovation Fund [SAST2016092]
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The selection of training samples is important for the accuracy and efficiency of the synthetic aperture radar (SAR) image change detection task. However, training samples are traditionally extracted from the whole image, which leads to longer training time and an unbalanced number of pixels in the changed and unchanged classes. To overcome this problem, we propose a novel change detection method combining saliency detection with a principal component analysis network, named SDPCANet. To enhance the reliability of the training samples and reduce the amount of training samples, the SDPCANet uses context-aware saliency detection to obtain the salient region, from which the training samples are extracted. In addition, to alleviate the gap between the numbers of training samples in two classes, we regulate the candidate samples using the uniform-selecting strategy to enhance the reliability of the training samples for the SDPCANet. Then, the SDPCANet is trained with the extracted training samples and the remaining pixels are classified in the salient region to obtain the final change map. The experimental results on four sets of multitemporal SAR images demonstrate that the SDPCANet outperforms the reference methods proposed recently.
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