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

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...

AI Technical Summary

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 specific mathematical analysis. How to deal with hyperspectral image texture from the perspective of geometric analysis is still a challenging problem

Images