High-resolution remote sensing image segmentation method

Abstract

The invention provides a high-resolution remote sensing image segmentation method. The method comprises steps that a to-be-segmented high-resolution remote sensing image is read; a Gaussian type-2 fuzzy membership function model of each ground feature category is utilized to calculate a Gaussian type-2 fuzzy membership degree corresponding to each gray level; a segmentation decision model of each ground feature category is utilized to calculate a membership degree of each gray level in each segmentation decision model; a ground feature category of a largest membership degree value of a gray level of each pixel in the segmentation decision models is a segmentation result; the Gaussian type-2 fuzzy membership function model is changed according to set step length, all the segmentation results are compared, a segmentation result with highest segmentation precision is taken as a final high-resolution remote sensing image segmentation result. According to the method, a segmentation problem caused by gray level membership uncertainty and segmentation decision uncertainty can be effectively solved, more precise fitting of high-resolution remote sensing data complex histogram distribution characteristics is realized, a noise problem is eliminated, and segmentation precision is improved.

Description

technical field [0001] The invention belongs to the field of image processing, in particular to a high-resolution remote sensing image segmentation method. Background technique [0002] Image segmentation is the most basic task in the process of remote sensing image processing, and it has always been a hot and difficult issue in image processing. The rich details of ground objects and targets presented by high-resolution remote sensing images have great potential and advantages in the precise segmentation of large-scale ground objects. However, the high-resolution feature also brings new segmentation problems: (1) In the high-resolution remote sensing image, the variability of the pixel spectral measurement of the same object in the high-resolution remote sensing image increases, which makes the same type of object appear asymmetrical, multiple The peak distribution feature increases the uncertainty of the pixel category; (2) the similarity of different ground objects incre...

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Problems solved by technology

However, the high-resolution feature also brings new segmentation problems: (1) In the high-resolution remote sensing image, the variability of the pixel spectral measurement of the same object in the high-resolution remote sensing image increases, which makes the same type of object appear asymmetrical, multiple The peak distribution feature increases the uncertainty of the pixel category; (2) the similarity of different ground objects increases, and the overlapping area of ​​the spectral measurement distribution curves of different ground objects increases, resulting in an increase in the uncertainty of the segmentation decision

However, under normal circumstances, the closer the membership degree corresponding to each pixel is to the membership degree center, the smaller its uncertainty is, and the farther it is from the membership degree center, the greater its certainty is. The interval type II model assumes that all The assumption that elements have the same secondary membership does not hold true

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