Floods are among the most devastating natural hazards in the world, affecting more people and causing more property damage than any other natural phenomena. One of the important problems associated with flood monitoring is a flood extent extraction from satellite imagery, since it is impractical to acquire the flood area through field observations. This paper presents a new method to the flood extent extraction from synthetic-aperture radar (SAR) images that is based on intelligent computations. In particular, we apply artificial neural networks, selforganizing Kohonen's maps (SOMs), for SAR image segmentation and classification. We implemented our approach in a Grid system that was used to process data from three different satellite sensors: ERS-2/SAR during the flooding on the river Tisza, Ukraine and Hungary (2001), ENVISAT/ASAR WSM (Wide Swath Mode) and RADAR-SAT-1 during the flooding on the river Huaihe, China (2007).
This paper presents a technique for the assessment and mapping of land biodiversity by using remote sensing data. The proposed approach uses a fuzzy model that encapsulates different ecological factors influencing biodiversity. We implemented our approach as a web service for the Pre-Black Sea region of the Ukraine.
This paper examines different approaches to remote sensing images classification. Included in the study are statistical approach, in particular Gaussian maximum likelihood classifier, and two different neural networks paradigms: multilayer perceptron trained with EDBD algorithm, and ARTMAP neural network. These classification methods are compared on data acquired from Landsat-7 satellite. Experimental results showed that to achieve better performance of classifiers modular neural networks and committee machines should be applied.
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