NORCAMA: Change analysis in SAR time series by likelihood ratio change matrix clustering
Idioma
en
Article de revue
Este ítem está publicado en
ISPRS Journal of Photogrammetry and Remote Sensing. 2015-03, vol. 101, p. 247-261
Elsevier
Resumen en inglés
<p>This paper presents a likelihood ratio test based method of change detectionand classification for synthetic aperture radar (SAR) time series, namelyNORmalized Cut on chAnge criterion MAtrix (NORCAMA). This methodinvolves ...Leer más >
<p>This paper presents a likelihood ratio test based method of change detectionand classification for synthetic aperture radar (SAR) time series, namelyNORmalized Cut on chAnge criterion MAtrix (NORCAMA). This methodinvolves three steps: 1) multi-temporal pre-denoising step over the wholeimage series to reduce the effect of the speckle noise; 2) likelihood ratiotest based change criteria between two images using both the original noisyimages and the denoised images; 3) change classification by a normalized cutbased clustering-and-recognizing method on change criterion matrix (CCM).The experiments on both synthetic and real SAR image series show theeffective performance of the proposed framework.</p>< Leer menos
Palabras clave en inglés
Change detection
Change classification
SAR time series
Change criterion matrix
Normalized cut
Likelihood ratio test
Orígen
Importado de HalCentros de investigación