Multi-band high-resolution remote sensing image segmentation method based on gray scale co-occurrence matrix

Abstract

The invention discloses a multi-band high-resolution remote sensing image segmentation method based on a gray scale co-occurrence matrix. In texture images based on the gray scale co-occurrence matrix, precipitation watershed transformation is adopted for segmentation of each band image, and therefore band segmentation results are superposed. At last, a region merging strategy based on multi-band spectral information is provided for merging fragment regions in the segmentation results, and finally image segmentation is achieve. A high-resolution ALOS image and an SPOT 5 image are tested respectively through the method which is compared with a traditional segmentation method based on local area homogeneity gradients. As is shown in experimental results, edges of an object can be accurately positioned, over-segmentation and under-segmentation are effectively overcome, and higher segmentation accuracy and stability are achieved.

Description

technical field [0001] The invention relates to a multi-band high-resolution remote sensing image segmentation method based on a gray level co-occurrence matrix, and belongs to the technical field of remote sensing image segmentation. Background technique [0002] At present, high-resolution remote sensing images are widely used in many fields such as urban planning, environmental assessment, and military affairs, and object-oriented analysis methods are receiving more and more attention in the research of high-resolution remote sensing images. Effective image segmentation is the foundation and important guarantee of object-oriented analysis methods. At present, the segmentation methods of high-resolution remote sensing images are mainly divided into five categories: segmentation methods based on pixels, such as threshold method and clustering method; segmentation methods based on edge detection; segmentation methods based on regions; segmentation methods based on physical m...

AI Technical Summary

Problems solved by technology

On this basis, Chen Qiuxiao et al. proposed a novel high-resolution remote sensing image segmentation method based on local homogeneity gradients. This algorithm effectively solves the over-segmentation problem of watershed transform, but there are also the following problems: Too rough quantization leads to the serious loss of a large amount of detailed information in high-resolution remote sensing images, which is prone to under-segmentation, and it is difficult to accurately locate the edges of objects; because there are a large number of obj...

Images