Neural Shrinkage for wavelet-based SAR Despeckling

0

A clustering approach to heterogeneous change detection

Luigi Tommaso Luppino, Stian Normann Anfinsen, Gabriele Moser, Robert Jenssen, Filippo Maria Bianchi, Sebastiano Serpico, Gregoire Mercier

PDF: Neural shrinkage for wavelet-based SAR despeckling

Change detection in heterogeneous multitemporal satellite images is a challenging and still not much studied topic in remote sensing and earth observation. This paper focuses on comparison of image pairs covering the same geographical area and acquired by two different sensors, one optical radiometer and one synthetic aperture radar, at two different times. We propose a clustering-based technique to detect changes, identified as clusters that split or merge in the different images. To evaluate potentials and limitations of our method, we perform experiments on real data. Preliminary results confirm the relationship between splits and merges of clusters and the occurrence of changes. However, it becomes evident that it is necessary to incorporate prior, ancillary, or application-specific information to improve the interpretation of clustering results and to identify unambiguously the areas of change.

Share.

About Author

Synthetic Aperture Radar (SAR) or SAR Journal is an industry trade journal which tracks the worldwide SAR industry. We offer news, education, and insights to the SAR industry. We are operated, moderated and maintained by members of the SAR community.This profile is run by multiple moderators who all represent the SyntheticApertureRadar.com If you would like to submit news or have questions about a post please email us here: SyntheticApertureRadarmag@gmail.com and someone will get back to you.