Fisher judged null space based method for decomposing mixed pixels of high-spectrum remote sensing image

Abstract

The invention belongs to the technical field of remote sensing image processing, and particularly discloses a Fisher judged null space based method for decomposing mixed pixels of a high-spectrum remote sensing image. A Fisher judged null space method is provided aiming at the problem that the decomposition precision is reduced due to the phenomenon of same objects and different spectrums generally existing in the mixed pixel decomposition. The method comprises the following steps: analyzing a training sample consisting of pure pixel spectrums of an end member, constructing an intra-class scattering matrix null space of the training sample, making the spectrum difference in the end member become null, searching a judgment vector causing the scattering degree of the intra-class scattering matrix to be maximum in the null space, and making the separation degree of end member spectrums of different classes maximum so as to furthest reduce the decomposition error caused by the same objects and different spectrums. The method of the invention has particularly important application values in the aspects of high-precision surface feature decomposition of the high-spectrum remote sensing image and detection and identification of ground targets.

Description

technical field [0001] The invention belongs to the technical field of remote sensing image processing, and in particular relates to a hyperspectral remote sensing image mixed pixel decomposition method based on Fisher discriminant null space. Background technique [0002] Remote sensing is a new comprehensive technology developed in the 1960s. It is closely related to science and technology such as space, electron optics, computer, and geography. It is one of the most powerful technical means for studying the earth's resources and environment. In recent years, with the advancement of imaging technology, multi-band remote sensing images have been widely used in more and more fields. Due to the limitation of the spatial resolution of the hyperspectral remote sensing sensor, a pixel in the hyperspectral remote sensing image often covers a variety of ground objects, and the spectral value of the pixel is actually a mixture of several pure ground object spectral values. This ty...

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Problems solved by technology

In 2000, Asner et al. differentially processed the spectrum to reduce the spectral difference of endmembers[3]. In 2004, Wu et al. normalized the spectrum by dividing the spectrum by the spectral mean value of each band, and then decomposed the mixed pixels[ 4], but this kind of method has the problem that the physical meaning of the spectral processing method is not clear

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