Visual perception enlightening high-resolution remote-sensing image segmentation method

Abstract

The invention discloses a visual perception enlightening high-resolution remote-sensing image segmentation method, and the method enables an object boundary in an image to be divided into an intensity boundary corresponding to a spectrum homogeneous area, and a texture boundary corresponding to a spectrum change area, and respectively extracts two types of visual information which serve as the main basis of segmentation. The method comprises the steps: filtering noise and texture information in the image through a nonlinear filtering method, and obtaining the intensity gradient through a gradient operator; analyzing the texture features of the image through employing a Gabor filter, enabling the filtering output of a plurality of channels to be merged and processed through the gradient operator, and obtaining the texture gradient of the image; carrying out the fusion of the intensity gradient and the texture gradient, carrying out the conversion of the gradient image after fusion through watershed conversion, and achieving the segmentation of the image. Compared with the prior art, the method improves the boundary accuracy of image segmentation, reduces the over-segmentation and under-segmentation phenomena, and can be effectively used in the field of high-resolution remote-sensing image information processing.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a segmentation method for simulating a visual perception mechanism in the technical field of high-resolution remote sensing image segmentation. The invention is mainly used for the segmentation of high-resolution satellite remote sensing images and aerial remote sensing images, so as to achieve the purpose of extracting ground object information in the images. Background technique [0002] High-resolution remote sensing images can provide rich details of ground objects, but also increase the internal spectral differences in homogeneous regions and present diverse texture characteristics. In addition, the sensor will introduce noise during the imaging process, which increases the accuracy of remote sensing images. Difficulty of segmentation. In recent years, high-resolution remote sensing images are mainly based on the Geographic Object-Oriented Image Analysis (GEO...

AI Technical Summary

Problems solved by technology

The method is based on the fact that the human visual system can treat the textured area and the spectrally uniform area as a whole, extracting the boundary of the spectrally uniform area and the boundary of the spectrally changing textured area in the image respectively, and then effectively fusing the two to achieve image segmentation , solve the problems of using single spectral information and inaccurate boundary positioning in the existing remote sensing image segmentation methods, improve the segmentation accuracy of remote sensing segmentation, and reduce the post-processing complexity in the process of remote sensing information extraction

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