Abstract
ABSTRACTIn 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.
Highlights
Change detection is a process which uses multi-temporal remote sensing images to obtain the change information of the same area, that is to say, to qualitatively analyze and confirm the features and processes of the change of the earth’s surface from the remote sensing data taken at different times (Radke, Andra, Al-Kofahi, & Roysam, 2005; Singh, 1989)
A lot of isolated noise are distributed over non-change region of detection result map shown in Figure 5(a) and Figure 5(b) achieved by probabilistic patch-based (PPB)-K algorithm and CIVB-F algorithm, respectively
A new synthetic aperture radar (SAR) image change detection algorithm is proposed in this article
Summary
Change detection is a process which uses multi-temporal remote sensing images to obtain the change information of the same area, that is to say, to qualitatively analyze and confirm the features and processes of the change of the earth’s surface from the remote sensing data taken at different times (Radke, Andra, Al-Kofahi, & Roysam, 2005; Singh, 1989). Ma et al proposed a change detector to fuse the log-ratio (LR) and the mean-ratio (MR) images by a context independent variable behavior operator (Ma et al, 2014.) This method improves detection precision, the running time is increased. We use the proposed adaptive threshold denoising algorithm in NSST domain to suppress noise for the final difference image. In order to keep the detail information of the difference image, an adaptive threshold denoising algorithm based on NSST domain is proposed in the article. (2) The denoised high frequency sub-band coefficients are obtained by the proposed adaptive threshold denoising algorithm. (3) The reconstructed difference image R0 is obtained by inverse NSST using the low frequency sub-band coefficient C and the high frequency sub-band coefficientsC0ðl;kÞ: K-means clustering algorithm. According to the Euclidean distance based on formula (12), we can acquire the change detection image
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