Hyperspectral images, which are obtained by imaging spectrometer, are datacubes. They contain both2-D spatial information and1-D spectral information.Because of large amount, they must be effectively compressed for the convenienceof transmission and storage. Moreover, hyperspectral images are the key datasource for ground object spectra analysis. Lossy compression is bound to loseuseful information, so they should be
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The rapid development and wide application of the Remote sensing is one of the most iconic scientific and technological achievements in 20th century, the radar and hyperspectral are particularly important. Hyperspectral remote sensing provides us with more information, the processing of remote sensing image processing has become a hot research field, and used in military
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With the rapid development of remote sensing technology and the continuous advancement of new sensors, the capability of obtaining data on remote sensing picture increases evidently. We can get a lot of information on different scales, different spectrum and different time in the same area. Hyperspectral data and high spatial resolution image are the most
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High performance detection equipment needs target signal characteristic value which is much preciser and contains a larger amount of data in order to detect dim targets and camouflaged targets. Hyperspectral information has the advantage of high spatial and spectral resolution, so this spatial and spectral characteristics can be used to distinguish dim targets in complex
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With much richer information and higher spectral resolution than multispectral image, hyperspectral image could resolve many problems that multispectral image could not do. But higher spectral resolution is accompanied by a huge volume of image data, which will result in excessive computing time and data complexity for transmission and storage, so it is necessary to
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With the development of the techniques of sensors, the spectral resolution of remote sensing images is increasing continually. The creation of hyperspectral images is an important leap in the field of remote sensing. Hyperspectral remote techniques keep ahead for the present and a period of time in the future in the field of remote sensing,
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Hyperspectral has drawn a lot of attention for its powerful ability to detect different materials. But the mass data, produced by hyperspectral sensor, has brought heavy burden on the transmission and storage system of the satellite. And the gap between data generating rates and transfer rates will become larger in future. At the same time,
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Remotely sensed hyperspectral image is a 3D stereoscopic image, that is to say, having another dimensional spectrum information again on the foundation of common and two-dimensional picture. So the data of remotely sensed hyperspectral image is huge and it is hard to deliver and saving directly. So compression to the image is necessary. Because the
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In this thesis,the basic concept of hyperspectral image is illustrated,the characteristics of hyperspectral image ,which are stronger spectrum dependence of hyperspcetral and hybrid image-spectrum ,are studied and vertified;then the difficulties of hyperpectral image processing is analyzed; at last current technique of dimensionality reduction technique are summarized. Through summarizing current techniques of hyperspectral image segmentation ,a
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The classification of ground objects is the basic content of hyperspectral image processing. In the real applications, the number of training samples is always limited. Statistical learning theory, the first theory that systematically studies the problem of machine learning with small size samples, put forward a kind of general machine learning method—-Support Vector Machine(SVM). In
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