Novel automatic selection method of optimal segmentation scale of high-resolution remote sensing image

A high-resolution, remote-sensing image technology, applied in the field of image processing, can solve the problems of single pixel information, long computing time, misclassification of pixels, etc., and achieve the effect of high calculation efficiency, high precision of segmentation quality, and strong adaptability

Inactive Publication Date: 2017-05-10
HUBEI UNIV OF TECH
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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 ident

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  • Novel automatic selection method of optimal segmentation scale of high-resolution remote sensing image
  • Novel automatic selection method of optimal segmentation scale of high-resolution remote sensing image
  • Novel automatic selection method of optimal segmentation scale of high-resolution remote sensing image

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Embodiment Construction

[0052] In order to make the technical means, creative features, goals and effects achieved by the present invention easy to understand, the present invention will be further described below in conjunction with the accompanying drawings and specific examples.

[0053] 1. Technical implementation plan

[0054] Input: graph D=(V, E, W) under the image, where V, E and W represent the vertex set, edge set and similarity matrix of graph D respectively;

[0055] Output: the image of optimal scale segmentation, all scale segmentation parameters;

[0056] 1) Use the watershed method to obtain the over-segmented image as the most fine-grained segmented image D0;

[0057] 2) extracting the spectrum, color, texture and other feature values ​​of the object in the above image;

[0058] 3) Estimate the GMM parameters using the expectation-maximization EM algorithm

[0059] 4) For 1=L to 0, execute steps 3)-4):

[0060] (1) In the first-layer MRF model, the calculation object message is i...

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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...

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Application Information

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IPC IPC(8): G06T7/10
Inventor 靳华中万方雷光波关峰刘潇龙黄磊李清
Owner HUBEI UNIV OF TECH
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