Journal
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
Volume 16, Issue 5, Pages 826-830Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2018.2879969
Keywords
Gabor wavelet transform; multivariate Gaussian mixture model (MGMM); Shannon entropy; synthetic aperture radar (SAR); unsupervised change detection; variational inference (VI)
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Funding
- National Natural Science Foundation of China [61871335, 61371165, 61771351]
- Frontier Intersection Basic Research Project for the Central Universities [A0920502051814-5]
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In this letter, we propose an unsupervised change detection method for synthetic aperture radar (SAR) images based on variational multivariate Gaussian mixture model (MGMM) and Shannon entropy. First, the difference features are generated from the Gabor wavelet transform of two SAR images. In variational inference framework, the variational MGMM is first introduced to implement accurate modeling for the data distribution of difference features and to output responsibilities. Subsequently, spatial information is explored on the responsibilities to yield the contextual responsibilities for improving the accuracy and reliability of change detection. Then, a posteriori probabilities of the changed and unchanged classes are derived from the contextual responsibilities, and Shannon entropy, being directly related to the classification error rate, is proposed to determine the optimal index integer. Finally, the binary change mask is achieved by separating the pixels into the changed and unchanged classes. The experiments on three pairs of SAR images for describing urban sprawl and water bodies demonstrate the effectiveness of the proposed method.
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