10 September 2008 Unsupervised change detection for satellite images using dual-tree complex wavelet transform
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Abstract
In this paper, an unsupervised change detection method for satellite images is proposed. The algorithm exploits the inherent multiscale data structure of the dual-tree complex wavelet transform (DT-CWT) to individually decompose each input image into six directional subbands at each scale. Such representation is to facilitate better change detection. The difference resulted from the DT-CWT coefficients of two satellite images taken at two different time instances is analyzed automatically by unsupervised selection of the decision threshold that minimizes the total error probability of change detection, under the assumption that the pixels in the difference image are independent of one another. The change maps produced in different subbands are merged by using both inter- and intra-scale information. Furthermore, the proposed technique requires the knowledge of the statistical distributions of the changed and unchanged subband coefficients of the two images. To perform an unsupervised estimation of the statistical terms that characterizes these distributions, an iterative method based on the expectation maximization (EM) algorithm is proposed. For the performance evaluation, the proposed algorithm is exploited for both noise-free and noisy images, and the results show that the proposed method not only provides accurate detection of small changes but also demonstrates attractive robustness against noise interference.
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Turgay Celik, Kai-Kuang Ma, "Unsupervised change detection for satellite images using dual-tree complex wavelet transform", Proc. SPIE 7084, Satellite Data Compression, Communication, and Processing IV, 708409 (10 September 2008); doi: 10.1117/12.794363; https://doi.org/10.1117/12.794363
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