Hyperspectral image processing method based on textural feature strengthening

Abstract

The invention provides a hyperspectral image processing method based on feature strengthening. Textural feature matrixes composed of feature values of all pixels in two-dimensional images are used for describing the corresponding two-dimensional images; the textural feature matrixes of all the two-dimensional images are subjected to textural feature strengthening to obtain textural feature strengthened matrixes; main textural features are extracted according to all the textural feature strengthened matrixes to form a main textural feature vector; the main textural feature vector is used for representing a hyperspectral image. According to the hyperspectral image processing method based on feature strengthening, multi-wavelength information of the hyperspectral image is reasonably utilized, rich textural features can be captured accurately, texture details can be distinguished conveniently, and the hyperspectral image processing method is particularly suitable for the analysis of fine-texture images.

Description

technical field [0001] The invention belongs to the technical field of image processing, and relates to a hyperspectral image processing method based on texture feature enhancement. Background technique [0002] Hyperspectral images are three-dimensional images, including ordinary two-dimensional plane image information and wavelength information. While imaging the spatial characteristics of the target, each spatial pixel is dispersed to form dozens or even hundreds of narrow bands for continuous spectral coverage. A hyperspectral image is a three-dimensional hyperspectral image composed of two-dimensional images corresponding to several wavelengths. Because different components have different spectral absorption, the image will reflect a certain defect more significantly at a specific wavelength, and the spectral information can fully reflect the differences in the physical structure and chemical composition of the sample. [0003] Near-infrared hyperspectral spectroscopy...

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Problems solved by technology

[0004] At present, in terms of hyperspectral analysis, there are mainly the following three methods: 1) By selecting a small number of representative two-dimensional images from a number of spectral images for texture analysis, this method usually considers that images with good spectral reflectance values Has excellent texture characteristics, but this hypothesis lacks effective proofs in theory and practice

2) Directly apply the three-dimensional texture method. These three-dimensional methods are extended from the classic two-dimensional method by treating the wavelength as the third dimension. However, this method will cause a large amount of information loss because it is too rough

[0005] Hyperspectral texture features can be effectively represented by the third extension method. However, this method mainly has the following three challenges: 1) It is necessary to define a good method that can describe fine textures, which needs to satisfy an excellent texture description The basic properties of the child, such as rotation invariance, etc.

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