High-spectrum image texture analysis method based on V-GLCM (Gray Level Co-occurrence Matrix)

A texture analysis and hyperspectral technology, applied in the field of hyperspectral remote sensing image processing, can solve problems that are difficult to apply mathematical analysis

Inactive Publication Date: 2013-02-20
HOHAI UNIV
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Problems solved by technology

[0007] The above concept of texture solves the problem of mapping texture from single-band to multi-band, but it is difficult to apply to spe...

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  • High-spectrum image texture analysis method based on V-GLCM (Gray Level Co-occurrence Matrix)
  • High-spectrum image texture analysis method based on V-GLCM (Gray Level Co-occurrence Matrix)
  • High-spectrum image texture analysis method based on V-GLCM (Gray Level Co-occurrence Matrix)

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Embodiment Construction

[0058] Below in conjunction with accompanying drawing and specific embodiment, further illustrate the present invention, should be understood that these embodiments are only for illustrating the present invention and are not intended to limit the scope of the present invention, after having read the present invention, those skilled in the art will understand various aspects of the present invention Modifications in equivalent forms all fall within the scope defined by the appended claims of this application.

[0059] Aiming at the disadvantage that the two-dimensional moving window in the GLCM texture model can only calculate the single-band texture and ignore the texture information between adjacent bands, a moving cube is proposed as the window for the new model to calculate the texture, thereby transforming the two-dimensional pixel pair spatial relationship It is a three-dimensional space, which is the biggest characteristic of the V-GLCM texture analysis model of the prese...

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Abstract

The invention discloses a high-spectrum image texture analysis method based on a V-GLCM (Gray Level Co-occurrence Matrix), comprising the following steps of: selecting high-spectrum image data needing to be subjected to texture analysis; carrying out gray level range conversion on an original image; normalizing a gray level value to a certain range; selecting a suitable movable cubic window size and an angle parameter; taking statistical index information inside a movable cubic body as a texture characteristic of a cubic center picture element; utilizing a picture element relation in a movable cubic window to establish a co-occurrence matrix; carrying out index quantification counting on the established co-occurrence matrix and backfilling to the central position of the current movable cubic window, namely replacing the texture characteristic of the position; and continuously moving the cubic window and carrying out texture calculation and extraction on the whole image to obtain a V-GLCM texture image. According to the high-spectrum image texture analysis method disclosed by the invention, the image texture extracted by the method considers a relation between adjacent wave sections of a high-spectrum image, contains the texture characteristic of the adjacent wave sections, and can sufficiently represent the special properties of the high-spectrum data.

Description

technical field [0001] The invention belongs to the technical field of hyperspectral remote sensing image processing, and in particular relates to a hyperspectral image texture analysis method based on V-GLCM (Volume Gray Level Co-occurrence Matrix). Background technique [0002] Hyperspectral Remote Sensing (Hyperspectral Remote Sensing) refers to the technology of using many narrow electromagnetic wave bands to obtain data about objects. It is one of the major technological breakthroughs in earth observation in the last 20 years of the 20th century. Frontier technology of remote sensing during the year. Compared with conventional multispectral remote sensing, hyperspectral data has the characteristics of large data volume, many narrow bands, strong correlation between bands, more information redundancy, and map integration. However, it is precisely its massive data and high-dimensional features that bring great difficulties to the transmission and storage of hyperspectral...

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Application Information

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IPC IPC(8): G06T7/00
Inventor 苏红军
Owner HOHAI UNIV
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