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

Xin Su (1) , Charles-Alban Deledalle (2) , Florence Tupin (1) , Hong Sun (3)

1 LTCI - Laboratoire Traitement et Communication de l'Information
2 IMB - Institut de Mathématiques de Bordeaux
3 Wuhan University [China]

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.

Keywords: Change detection; Change classification; SAR time series; Change criterion matrix; Normalized cut; Likelihood ratio test

Domains: Image Processing [eess.IV]

Xin Su, Charles-Alban Deledalle, Florence Tupin, Hong Sun. NORCAMA: Change analysis in SAR time series by likelihood ratio change matrix clustering. ISPRS Journal of Photogrammetry and Remote Sensing, 2015, 101, pp.247-261. ⟨hal-01185740⟩