The potentials of SAR sensors in change detection applications have been recently strengthened by the high spatial 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 statistics 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.
Andrea Garzelli and Claudia Zoppetti, "A segmentation-based approach to SAR change detection and mapping," Proc. SPIE 10004, Image and Signal Processing for Remote Sensing XXII, 1000410 (Presented at SPIE Remote Sensing: September 28, 2016; Published: 18 October 2016); https://doi.org/10.1117/12.2242565.
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