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.
Highlights
The synthetic aperture radar (SAR) imaging process is not affected by sunlight, clouds, or the atmosphere because of the microwave imaging principle
An effective SAR image change detection algorithm based on saliencyguided convolutional neural networks was proposed
The saliency map was adopted to guide the search for the interest regions in the initial difference image computed by a log-ratio operator, and the noise in the saliency map could be suppressed to some extent with the Otsu method
Summary
The synthetic aperture radar (SAR) imaging process is not affected by sunlight, clouds, or the atmosphere because of the microwave imaging principle. As the result of these findings, for the sake of suppressing the speckle noise and preserving interest information, one saliency detection method is used to extract the interesting regions that probably pertain to the changed objects. To extract the changed information, the convolutional-wavelet neural networks (CWNNs) model is utilized to learn features from the denoised images and the difference image. A saliency detection model is used in the proposed method, which aims to generate the salient regions that probably belong to the changed objects. The saliency detection model can extract attractive and compact salient areas from the difference image with a simple operation. It can remove background pixels and suppress noise. A convolutional neural network based on dual-tree complex wavelet transform is constructed that aims to enhance the accuracy of change detection
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