Multi-scale multi-level image segmentation method based on minimum spanning tree

Abstract

The invention provides a multi-scale multi-level image segmentation method based on a minimum spanning tree. Multi-scale multi-level image segmentation is expressed and realized by a graph model. The segmentation method is suitable for initial segmentation results obtained by various images and various rules to effectively combine over-segmented regions in high level segmentation, so an over-segmentation phenomenon is avoided; moreover, multi-scale segmentation results on different levels provides characteristic description information on different levels for analysis of target structural components; and the method is very important for target recognition.

Description

Multi-scale and multi-level image segmentation method based on minimum spanning tree technical field The method belongs to the technical field of image processing and recognition, and in particular relates to a new multi-level and multi-scale pyramid image segmentation method based on minimum spanning tree optimization theory and graph model characteristics. Background technique High-spatial-resolution remote sensing images provide us with high-precision spatial geometric information, rich texture information, and multi-spectral information of the ground landscape, making traditional pixel-based remote sensing image classification methods inapplicable. Therefore, high-resolution remote sensing image processing Challenged with the detail provided by imagery. For this reason, Baatz and Schape pointed out in [1] in 1999 that important semantic interpretation needs to be represented by meaningful objects and the relationship between objects in images rather than by pixels. Th...

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Problems solved by technology

Aiming at the problem of multi-scale and multi-level image segmentation and description, in order to avoid the problem of blurred and inaccurate boundaries in multi-scale space, and to describe the adjacency relat

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