SAR image change detection based on equal weight image fusion and adaptive threshold in the NSST domain

• Main Authors: Zhou Wenyan, Jia Zhenhong, Yinfeng Yu, Jie Yang, Nilola Kasabov
• Format: Article
• Language: English
• Published: Taylor & Francis Group 2018-01-01
• Series: European Journal of Remote Sensing
• Subjects: Non-subsampled shearlet transform; image fusion; change detection; difference map; adaptive threshold; k-mean algorithm
• Online Access: http://dx.doi.org/10.1080/22797254.2018.1491804

In order to improve the accuracy of change detection and reduce the running time, a change detection method based on equal weight image fusion and adaptive threshold in the NSST domain is proposed. First, the logarithmic transformation is used to transform images and the mean filter is applied to the transformed images. The log-ratio method and the mean ratio method are adopted to generate two kinds of difference images. The final difference image is achieved by equal weight image fusion method. Then, an adaptive threshold denoising method based on non-subsampled shearlet transform (NSST) is used to achieve noise reduction. Finally, the k-means clustering algorithm is utilized to get the change detection results. The experimental results show that the proposed algorithm has better change detection performance than the reference algorithms in visual effect and objective parameters.