Version 1
: Received: 10 July 2020 / Approved: 11 July 2020 / Online: 11 July 2020 (09:49:37 CEST)
How to cite:
Vasile, G. Independent Component Analysis Data Processing Framework for Polarimetric Synthetic Aperture Radar Images. Preprints2020, 2020070237. https://doi.org/10.20944/preprints202007.0237.v1
Vasile, G. Independent Component Analysis Data Processing Framework for Polarimetric Synthetic Aperture Radar Images. Preprints 2020, 2020070237. https://doi.org/10.20944/preprints202007.0237.v1
Vasile, G. Independent Component Analysis Data Processing Framework for Polarimetric Synthetic Aperture Radar Images. Preprints2020, 2020070237. https://doi.org/10.20944/preprints202007.0237.v1
APA Style
Vasile, G. (2020). Independent Component Analysis Data Processing Framework for Polarimetric Synthetic Aperture Radar Images. Preprints. https://doi.org/10.20944/preprints202007.0237.v1
Chicago/Turabian Style
Vasile, G. 2020 "Independent Component Analysis Data Processing Framework for Polarimetric Synthetic Aperture Radar Images" Preprints. https://doi.org/10.20944/preprints202007.0237.v1
Abstract
The Independent Component Analysis (ICA) has been recently introduced as a reliable alternative to identify canonical scattering mechanisms within Polarimetric Synthetic Aperture Radar (PolSAR) images. This manuscript addresses two important aspects when applying such methods on real data, namely speckle filtering and statistical classification with ICA. A novel PolSAR data processing framework is introduced by adjusting the Lee's sigma filter to the particular nature of the Touzi's polarimetric decomposition. In its current form, it allows the use of the ICA mixing matrix in the derived speckle filter. An extension of the Fromont at al. iterative segmentation is introduced, equally. This proposed framework is tested using P band airborne PolSAR data acquired for the ESA campaign TropiSAR campaign.
Computer Science and Mathematics, Computer Vision and Graphics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
