Titlebook: Computational Intelligence for Remote Sensing; Manuel Graña,Richard J. Duro Book 2008 Springer-Verlag Berlin Heidelberg 2008 Markov.Markov

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https://doi.org/10.1007/978-3-658-29122-8 the literature regarding remote sensing images. Our own focus is on hyperspectral images. The approach we are pursuing is that of characterizing the spectral content of the image through the set of endmembers induced from it. We describe some ideas and numerical experiments for a system that would

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https://doi.org/10.1007/978-3-319-18005-2 of the gathered information. The target areas are characterized by zones with different interest levels. We model the interest variable as an “importance function” that assigns a quantitative reference to each point. Due to the nature of WSNs, the sensor deployment must guarantee that the informati

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Sergei A. Samoilenko,Marlene Laruelleest-generation Earth observation instruments is expected to introduce extremely high computational requirements in neural network-based algorithms for classification of high-dimensional data sets such as hyperspectral images, with hundreds of spectral channels and very fine spatial resolution. A sig

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https://doi.org/10.1007/978-3-030-27253-1fferent domains because data accessibility is limited to queries based on geographical coordinates, time of acquisitions, sensor types, and acquisition mode. Research in knowledge discovery from remote sensing archives has been propelled in the last few years by the adoption of newer technologies. H

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https://doi.org/10.1007/978-3-030-27253-1 a total burnt area of 740,000 hectares [22]. Even if in some countries according to the national burnt area statistics the problem seems to be under control and of steady or decreasing magnitude, in regions like the western Iberian Peninsula or large parts of North America the trend is worryingly o

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