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

起皺紋

https://doi.org/10.1007/978-3-030-27253-1hives then in multimedia image archives. Hence, the adoption of new technologies that allow the accessibility of remote sensing data based on content and semantics is required to overcome this challenge and increase useful exploitation of the data [1, 2, 3].

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Computational Intelligence for Remote Sensing978-3-540-79353-3Series ISSN 1860-949X Series E-ISSN 1860-9503

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創(chuàng)作

Remote Sensing Data Compression,e a special challenge, and multiple efficient image compression systems have appeared. This chapter contributes an overview of several techniques for image coding systems, focusing on lossy approaches.

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Public Health Communication and Growth, allowing to obtain a spectral signature of each point in the scene. They are applied to perform many different tasks such as accurate mapping of wide areas, object identification and recognition, target detection, process monitoring and control, clinical diagnosis imaging and environment assessmen

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Albert Anani-Bossman,Isaac Abeku Blanksonn the remote sensing community. Watermarking techniques help to solve the problems raised by this issue. In this paper we elaborate on the proposition of an optimal placement of the watermark image in a hyperspectral image. We propose an evolutionary algorithm for the digital semi-fragile watermakin