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In the context of the use of remote sensed data for monitoring land cover it is very important to develop methodologies to obtain reliable maps. In order to achieve this objective a possible approach is to combine both " spectral " and " spatial " features to characterizing each ground class. In this paper we propose the integration of a spectral classifier(More)
—This paper presents a retrieval algorithm that estimates spatial and temporal distribution of volumetric soil moisture content, at an approximate depth of 5 cm, using multi-temporal ENVISAT Advanced Synthetic Aperture Radar (ASAR) alternating polarization images, acquired at low incidence angles (i.e., from 15 to 31). The algorithm appropriately(More)
In this paper a new approach to performing change detection analyses based on a combination of supervised and unsupervised techniques is presented. Two remotely sensed, independently classified images are compared. The change estimation is performed according to the Post Classification Comparison (PCC) method if the posterior probability values are(More)
A new, to our knowledge, algorithm for the phase unwrapping (PU) problem that is based on stochastic relaxation is proposed and analyzed. Unlike regularization schemes previously proposed to handle this problem, our approach dispells the following two assumptions about the solution: a Gaussian model for noise and the magnitude of the true phase-field(More)
Fuzzy learning vector quantization (FLVQ), also known as the fuzzy Kohonen clustering network, was developed to improve performance and usability of on-line hard-competitive Kohnen's vector quantization and soft-competitive self organizing map (SOM) algorithms. The FLVQ effectiveness seems to depend on the range of change of the weighting exponent m(t). In(More)