Loading [MathJax]/extensions/TeX/extpfeil.js
Adaptive Contourlet Fusion Clustering for SAR Image Change Detection | IEEE Journals & Magazine | IEEE Xplore

Adaptive Contourlet Fusion Clustering for SAR Image Change Detection


Abstract:

In this paper, a novel unsupervised change detection method called adaptive Contourlet fusion clustering based on adaptive Contourlet fusion and fast non-local clustering...Show More

Abstract:

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.
Published in: IEEE Transactions on Image Processing ( Volume: 31)
Page(s): 2295 - 2308
Date of Publication: 04 March 2022

ISSN Information:

PubMed ID: 35245194

Funding Agency:


References

References is not available for this document.