A New Automatic Selection Method of Optimal Segmentation Scale for High Resolution Remote Sensing Image

Abstract

The invention discloses a novel automatic selection method of an optimal segmentation scale of a high-resolution remote sensing image. A multi-scale segmentation model of a high-resolution image is established by using a multi-scale MRF model, meanwhile image layer segmentation and image plane modeling are carried out at the same time, and context information between layer and layer objects and the spatial dependency of objects in the same layer are described. Spectrums, colors, textures, topological relations and other basic features of the objects are normalized in a Markov random field, a global optimal segmentation scale selection method capable of being automatically executed by a computer is realized by probabilistic information convergence calculation, parameter selection calculation and inference engineering are automatically executed by the computer, and an optimal segmentation scale parameter on theory is obtained. The technique has the advantages of high segmentation quality precision, high self adaptability and high computational efficiency.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a new method for automatically selecting the optimal segmentation scale of high-resolution remote sensing images. Background technique [0002] Object-oriented feature information extraction is the foundation and premise of high-resolution remote sensing image analysis. Due to the rich information contained in high-resolution remote sensing images, complex types of ground objects, and different types of ground objects correspond to different segmentation scales, a single segmentation scale cannot meet the application requirements. In object-oriented analysis of high-resolution remote sensing images, multi-scale image segmentation methods are often used to provide corresponding scales for different types of features. In order to ensure the accuracy of image information extraction, it is necessary to understand the effect of image information as the segmentation scale cha...

AI Technical Summary

Problems solved by technology

However, the existing image MRF models often use a top-down approach to construct the hierarchical structure of the image. Since the establishment of the most fine-grained scale depends on the identification calculation of all pixels in the image, it brings the problem of long calculation time, and because the pixel information Relatively simple, it is easy to cause misclassification of pixels

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