NORCAMA: Change analysis in SAR time series by likelihood ratio change matrix clustering

Abstract

This paper presents a likelihood ratio test based method of change detection and classification for synthetic aperture radar (SAR) time series, namely NORmalized Cut on chAnge criterion MAtrix (NORCAMA). This method involves three steps: (1) multi-temporal pre-denoising step over the whole image series to reduce the effect of the speckle noise; (2) likelihood ratio test based change criteria between two images using both the original noisy images and the denoised images; (3) change classification by a normalized cut based clustering-and-recognizing method on change criterion matrix (CCM). The experiments on both synthetic and real SAR image series show the effective performance of the proposed framework.