Synthetic Aperture Radar Image Change Detection Based on Principal Component Analysis and Two-Level Clustering

Synthetic aperture radar (SAR) change detection provides a powerful tool for continuous, reliable, and objective observation of the Earth, supporting a wide range of applications that require regular monitoring and assessment of changes in the natural and built environment. In this paper, we introdu...

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Bibliographic Details
Published inRemote sensing (Basel, Switzerland) Vol. 16; no. 11; p. 1861
Main Authors Li, Liangliang, Ma, Hongbing, Zhang, Xueyu, Zhao, Xiaobin, Lv, Ming, Jia, Zhenhong
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.06.2024
Subjects
Algorithms
Built environment
Change detection
Cluster analysis
Clustering
Comparative analysis
Eigenvalues
Eigenvectors
Feature extraction
Gabor wavelets
Image processing
Methods
Morlet wavelet
Neural networks
principal component analysis
Principal components analysis
Radar
Radar detection
Radar imaging
Remote sensing
SAR
Synthetic aperture radar
Technology application
two-level clustering
Urban environments
China
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Summary:Synthetic aperture radar (SAR) change detection provides a powerful tool for continuous, reliable, and objective observation of the Earth, supporting a wide range of applications that require regular monitoring and assessment of changes in the natural and built environment. In this paper, we introduce a novel SAR image change detection method based on principal component analysis and two-level clustering. First, two difference images of the log-ratio and mean-ratio operators are computed, then the principal component analysis fusion model is used to fuse the two difference images, and a new difference image is generated. To incorporate contextual information during the feature extraction phase, Gabor wavelets are used to obtain the representation of the difference image across multiple scales and orientations. The maximum magnitude across all orientations at each scale is then concatenated to form the Gabor feature vector. Following this, a cascading clustering algorithm is developed within this discriminative feature space by merging the first-level fuzzy c-means clustering with the second-level neighbor rule. Ultimately, the two-level combination of the changed and unchanged results produces the final change map. Five SAR datasets are used for the experiment, and the results show that our algorithm has significant advantages in SAR change detection.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs16111861