Blonda, P; Satalino, G; Wasowski, J; Parise, M; Baraldi, A; Refice, A, 1999, Neural techniques for SAR intensity and coherence data classification., EOS-SPIE Symposium on Image and Signal Processing for Remote Sensing V, pp. 374–381, University of Florence, Italy, Sept. 20-24, 1999,
URL: http://www.cnr.it/prodotto/i/235097

In recent years it has been proved that combined analysis of SAR intensity and interferometric correlation images is a valuable tool in classification tasks where traditional techniques such as crisp thresholding schemes and classical maximum likelihood classifiers have been employed. In this work, developed in the framework of the ESA AO3-320 project titled Application of ERS data to land slide activity monitoring in southern;Apennines, Italy, our goal is to investigate_ 1) usefulness of SAR interferometric correlation information in mapping areas with diffuse erosional activity, including Landslides; and 2) effectiveness of soft computing techniques in the combined analysis of SAR intensity and interferometric correlation images. Two neural classifiers are selected from the literature. The first classifier is a one-stage error-driven Multilayer Perceptron (MLP) and the second classifier is a Two-Stage Hybrid (TSH) learning system, consisting of a sequence of an unsupervised data-driven first stage with a supervised error-driven second stage. The TSH unsupervised first stage is implemented as either_ a) the on-line learning, dynamic-sizing, dynamic-linking Fully Self Organizing Simplified Adaptive Resonance Theory (FOSART) clustering model; b) the batch-learning, static-sizing, no-linking Fuzzy Learning Vector Quantization (FLVQ) algorithm; or c) the on-line learning, static-sizing, static-linking Self-Organizing Map (SOM). The input data set consists of three SAR ERS-1/ERS-2 tandem pair images depicting an area featuring slope instability phenomena in the Campanian Apennines of Southern Italy. From each tandem pair, four pixel-based features are extracted_ the backscattering mean intensity, the interferometric coherence, the backscattering intensity texture and the backscattering intensity change. Our classification task is focused on the discrimination of land cover types useful for hazard evaluation, i.e,, evaluation of areas affected by erosion. Classification results show that class erosion can be discriminated from other land cover classes when SAR mean intensity images are combined with coherence and texture information. In addition, our results demonstrate that soft computing techniques provide useful tools for the combined analysis of SAR intensity and coherence images. In particular, the TSH classifier employing the FOSART clustering algorithm shows_ i) an overall accuracy comparable with that of the other classification schemes under testing; ii) a training cost significantly lower than that of MLP and lower than that of TSH employing either FLVQ or SOM as its first stage; and iii) a capability of discriminating class erosion superior to that of the other classification schemes under testing.

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