Paper Info
Title
Change Detection From Sar Images Based On Convolutional Neural Networks Guided By Saliency Enhancement
Abstract
Change detection is an important task in identifying land cover change in different periods. In synthetic aperture radar (SAR) images, the inherent speckle noise leads to false changed points, and this affects the performance of change detection. To improve the accuracy of change detection, a novel automatic SAR image change detection algorithm based on saliency detection and convolutional-wavelet neural networks is proposed. The log-ratio operator is adopted to generate the difference image, and the speckle reducing anisotropic diffusion is used to enhance the original multitemporal SAR images and the difference image. To reduce the influence of speckle noise, the salient area that probably belongs to the changed object is obtained from the difference image. The saliency analysis step can remove small noise regions by thresholding the saliency map, and interest regions can be preserved. Then an enhanced difference image is generated by combing the binarized saliency map and two input images. A hierarchical fuzzy c-means model is applied to the enhanced difference image to classify pixels into the changed, unchanged, and intermediate regions. The convolutional-wavelet neural networks are used to generate the final change map. Experimental results on five SAR data sets indicated the proposed approach provided good performance in change detection compared to state-of-the-art relative techniques, and the values of the metrics computed by the proposed method caused significant improvement.
Year
DOI
Venue
2021
10.3390/rs13183697
REMOTE SENSING
Keywords
DocType
Volume
synthetic aperture radar image, change detection, saliency detection, convolutional-wavelet neural networks, hierarchical fuzzy c-means
Journal
13
Issue
Citations 
PageRank 
18
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Liangliang Li154.17
Ma Hongbing2378.17
Zhenhong Jia32915.13