SAR Image Change Detection Using PCANet Guided by Saliency Detection

Mengke Li, Ming Li, Peng Zhang, Yan Wu, Wanying Song, Lin An

Research output: Contribution to journalArticlepeer-review

Abstract

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.
Original languageEnglish
Pages (from-to)402-406
Number of pages5
JournalIEEE Geoscience and Remote Sensing Letters
Volume16
Issue number3
DOIs
Publication statusPublished - 1 Mar 2019
Externally publishedYes

User-Defined Keywords

  • Training
  • Synthetic aperture radar
  • Feature extraction
  • Saliency detection
  • Principal component analysis
  • Reliability

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