Soft computing techniques for data classification in a landslide-prone area of Italy

Single and repeat-pass SAR interferometry appears to be a new promising tool for a wide variety of applications. Recent studies have already shown that interferometric correlation, e.g. coherence and intensity images can be coupled to improve thematic land classification. The techniques used to combine and analyze such information are mainly based on thresholding schemes and classical maximum likelihood, although SAR data are not normally distributed. The main objective of this work is the validation of soft computing techniques for the analysis of ERS SAR intensity images combined with interferometric correlation images. A multi-modular neural system, composed by unsupervised and supervised architectures was applied to the analysis of one tandem ERS 1/2, pair, 27-28 August 1995, and one single ERS 2 image, 24 July 1995.