The potentials of SAR sensors in change detection applications have been recently strengthened by the high spa- tial resolution and the short revisit time provided by the new generation SAR-based missions, such as COSMO- SkyMed, TerraSAR-X, and RadarSat 3. Classical pixel-based change detection methods exploit first-order statis- tics variations in multitemporal acquisitions. Higher-order statistics may improve the reliability of the results, while plain object-based change detection are rarely applied to SAR images due to the low signal-to-noise ratio which characterizes 1-look VHR SAR image products. The paper presents a hybrid approach considering both a pixel-based selection of likely-changed pixels and a segmentation-driven step based on the assumption that structural changes correspond to some clusters in a multiscale amplitude/texture representation. Experiments on simulated and true SAR image pairs demonstrate the advantages of the proposed approach.
|Titolo:||A segmentation-based approach to SAR change detection and mapping|
GARZELLI, ANDREA (Corresponding)
|Citazione:||Garzelli, A., & Zoppetti, C. (2016). A segmentation-based approach to SAR change detection and mapping. In Image and Signal Processing for Remote Sensing XXII (pp.1000401-1000410). Bellingham : SPIE-INT soc optical engineering.|
|Appare nelle tipologie:||4.1 Contributo in Atti di convegno|