Quick high-resolution remote sensing image segmentation method

Abstract

The invention relates to the field of image processing, and discloses a quick high-resolution remote sensing image segmentation method. The method comprises steps: S1, the high-resolution remote sensing image is read; S2, a multiband morphological gradient for the read high-resolution remote sensing image is calculated to obtain a multiband morphological gradient image formed by the multiband morphological gradient; S3, morphological rebuilding is carried out on the multiband morphological gradient image to obtain a gradient image after morphological rebuilding; S4, watershed segmentation is carried out on the gradient image after morphological rebuilding to obtain an image after watershed segmentation; and S5, region merging is carried out on the image after watershed segmentation. The remote sensing image with mass data can be quickly segmented, and over segmentation can be effectively reduced.

Description

technical field [0001] The invention relates to the field of image processing, in particular to a method for fast segmentation of high-resolution remote sensing images. Background technique [0002] Image segmentation is a key technology in the field of image processing. At present, common image segmentation methods can be divided into three categories: segmentation methods based on graph theory, segmentation methods based on gradient descent, and segmentation methods based on energy functionals. Among them, the segmentation method based on graph theory is better, but the efficiency is generally low and requires a lot of memory; the method based on gradient descent is more efficient, but it is not closely related to the semantic information of the image, and it is more prone to over-segmentation; However, segmentation methods based on energy functionals often need to formulate corresponding energy functions according to specific application backgrounds, which is poor in app...

AI Technical Summary

Problems solved by technology

[0005] However, there are still some problems in the application of the above segmentation techniques

Take watershed segmentation as an example: the gradient image used in watershed segmentation is generally obtained by the traditional gray gradient calculation method, which does not make good use of the multi-band spectral information of remote sensing images; the traditional immersion method watershed segmentation cannot control the immersion speed, and the interference of pseudo extreme points brought by noise cannot be ruled out

In addition, all the above segmentation algorithms have a common problem, that is, they cannot handle remote sensing images with massive data.

This is because all the above-mentioned segmentation algorithms need to load a large amount of remote sensing image data into the memory at one time for analysis and processing, which makes these segmentation algorithms may be abnormal when the data volume of remote sensing images is too large, and the segmentation speed will drop suddenly, and bring difficulties to the rapid processing of massive data (such as the fusion data of the whole scene)

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