Skip to Main Content
This paper reports on monitoring land cover in the urban and sub-urban area of Rome, Italy, by multi-temporal ERS 1-2 SLC SAR images. The identification of the SAR image parameters, including backscattering, degree of interferometric coherence and textural pixel-based features to be exploited in classification, is discussed. The information extracted from the SAR images is fused and processed by a Multi-Layer Perceptron (MLP) neural network (NN) to produce land cover maps. The network topology has been carefully designed, paying special attention to the number of hidden units. Once trained, the net's performance has been validated over a statistically significant ensemble of patterns independent of the learning set. The net has been used for the automatic classification of a large area in two different years, 1994 and 1999, thus obtaining a map of hot spots corresponding to areas where changes had occurred.