Focusing on a Mediterranean Natura 2000 site in Italy, the effectiveness of the cross correlation analysis (CCA) technique for quantifying change in the area of semi-natural grasslands at different spatial resolutions (grain) was evaluated. In a fine scale analysis (2 m), inputs to the CCA were a) a semi-natural grasslands layer extracted from an existing validated land cover/land use (LC/LU) map (1:5000, time T1) and b) a more recent single date very high resolution (VHR) WorldView-2 image (time T2), with T2 > T1. The changes identified through the CCA were compared against those detected by applying a traditional post-classification comparison (PCC) technique to the same reference T1 map and an updated T2 map obtained by a knowledge driven classification of four multi-seasonal Worldview-2 input images. Specific changes observed were those associated with agricultural intensification and fires. The study concluded that prior knowledge (spectral class signatures, awareness of local agricultural practices and pressures) was needed for the selection of the most appropriate image (in terms of seasonality) to be acquired at T2. CCA was also applied to the comparison of the existing T1 map with recent high resolution (HR) Landsat 8 OLS images. The areas of change detected at VHR and HR were broadly similar with larger error values in HR change images.

This paper deals with the application of a new competitive, on-line, neuro-fuzzy architecture, the fully self-organizing simplified adaptive resonance theory (FOSART), to the analysis of remote sensed Antarctic data, in a classification experiment. FOSART employs fuzzy set memberships in the weights updating rule; it applies an ART-based vigilance test to control neuron proliferation and takes advantage of the fact that it employs a new version of the competitive Hebbian Rule to dynamically generate and remove synaptic links between neurons, as well as neurons. As a consequence, FOSART can develop disjointed subnets. The results obtained with FOSART have been compared with those obtained with other neuro-fuzzy unsupervised architecture: FuzzySART, FLVQ, SOM. The finding suggests that FOSART performances are lower, at convergence, than those of FLVQ and SOM, even if it shows a faster adaptivity to the input data structure, due to its topological and on-line characteristics.

We consider the problem of classification of remote sensed data from LANDSAT Thematic Mapper images. The data have been acquired in July 1986 on an area locate din South Italy. We compare the performance obtained by feed-forward neural networks designed by a parallel genetic algorithm to determine their topology with the ones obtained by means of a multi-layer perceptron trained with Back Propagation learning rule. The parallel genetic algorithm, implemented on the APE100/Quadrics platform, is based on the coding scheme recently proposed by Sternieri and Anelli and exploits a recently proposed environment for genetic algorithms on Quadrics, called AGAPE. The SASIMD architecture of Quadrics forces the chromosome representation. The coding scheme provides that the connections weights of the neural network are organized as a floating point string. The parallelization scheme adopted is the elitistic coarse grained stepping stone model, with migration occurring only towards neighboring processors. The fitness function depends on the mean square error.After fixing the total number of individuals and running the algorithm on Quadrics architectures with different number of processors, the proposed parallel genetic algorithm displayed a superlinear speedup. We report results obtained on a data set made of 1400 patterns.

This study investigates the applicability of a multimodular neuro-fuzzy system in the multispectral analysis of magnetic resonance (MR) images of the human brain. The system consists of two components: an unsupervised neural module for image segmentation in tissue regions and a supervised module for tissue labeling. The former is the fuzzy Kohonen clustering network (FKCN). The latter is a feed-forward network based on the back-propagation learning rule. The results obtained with the FKCN have been compared with those extracted by a self organizing map (SOM). The system has been used to analyze the multispectral MR brain images of a healthy volunteer. The data set included the proton density (PD), T2, T1 weighted spin-echo (SE) bands and a new T1- weighted three dimensional sequence, i.e. the magnetization- prepared rapid gradient echo (MP-RAGE). One of the main objectives of this study has been to evaluate the usefulness of brain imaging with the MP-RAGE sequence in view of automatic tissue classification. To this purpose, a quantitative evaluation has been provided on the base of some labeled areas selected interactively by a neuro- radiologist from the input raw images. Quantitative results seem to indicate that the MP-RAGE sequence may provide higher tissue separability than the T1-weighted SE sequence.