Journal
IEEE TRANSACTIONS ON IMAGE PROCESSING
Volume 31, Issue -, Pages 2295-2308Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIP.2022.3154922
Keywords
Speckle; Radar polarimetry; Wavelet transforms; Neural networks; Clustering algorithms; Synthetic aperture radar; Image segmentation; Synthetic aperture radar; unsupervised change detection; Contourlet fusion; non-local clustering
Funding
- Foundation for Innovative Research Groups of the National Natural Science Foundation of China [61621005]
- National Natural Science Foundation of China [61906093, 61802190, U1701267, 61573267, 61906150, 61902298]
- Fund for Foreign Scholars in University Research and Teaching Program's 111 Project [B07048]
- Major Research Plan of the National Natural Science Foundation of China [91438201, 91438103]
- Nature Science Foundation of Jiangsu Province, China [BK20190451]
- Open Research Fund in 2021 of Jiangsu Key Laboratory of Spectral Imaging and Intelligent Sense [JSGP202101]
- Fundamental Research Funds for the Central Universities [30919011279, JBF201905]
- Chinese Association for Artificial Intelligence (CAAI)-Huawei MindSpore Open Fund
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In this paper, a novel unsupervised change detection method based on adaptive Contourlet fusion and fast non-local clustering is proposed for multi-temporal SAR images. The method uses Contourlet fusion and fuzzy clustering to generate a binary image indicating changed regions, and then applies fast non-local clustering to classify the fused image.
In this paper, a novel unsupervised change detection method called adaptive Contourlet fusion clustering based on adaptive Contourlet fusion and fast non-local clustering is proposed for multi-temporal synthetic aperture radar (SAR) images. A binary image indicating changed regions is generated by a novel fuzzy clustering algorithm from a Contourlet fused difference image. Contourlet fusion uses complementary information from different types of difference images. For unchanged regions, the details should be restrained while highlighted for changed regions. Different fusion rules are designed for low frequency band and high frequency directional bands of Contourlet coefficients. Then a fast non-local clustering algorithm (FNLC) is proposed to classify the fused image to generate changed and unchanged regions. In order to reduce the impact of noise while preserve details of changed regions, not only local but also non-local information are incorporated into the FNLC in a fuzzy way. Experiments on both small and large scale datasets demonstrate the state-of-the-art performance of the proposed method in real applications.
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