Region-based multi-feature fusion high-resolution remote sensing image segmentation method

A multi-feature fusion, remote sensing image technology, applied in the field of image processing, can solve the problems of insufficient use of image features, poor adaptability, and low algorithm efficiency.

Active Publication Date: 2017-01-04
CHANGAN UNIV
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Problems solved by technology

[0008] In view of the defects or deficiencies in the above-mentioned prior art, the purpose of the present invention is to provide a region-based multi-feature fusion high-resolution remote sensing image segmentation method to solve the problems of insufficient utilization of image features and relatively low algorithm efficiency in the prior art. Problems and defects of low and poor adaptability

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[0072] In order to make the purpose, technical solution and advantages of the present invention clearer, the present invention will be further described in detail in conjunction with the accompanying drawings and embodiments. A region-based multi-feature fusion high-resolution remote sensing image segmentation method of the present invention specifically includes the following steps :

[0073] Step 1: Perform principal component analysis on the high-resolution remote sensing image to obtain the base image. After performing NSCT transformation on the base image, extract the texture feature vector of each point in the base image, and then perform fuzzy C-mean aggregation on the texture feature vectors of all points. class, get the clustering set;

[0074] Step 1.1: Perform principal component analysis on the high-resolution image, and select the first principal component as the base image I for NSCT transformation;

[0075] Step 1.2: Set the number of layers k (k is 2 to 5) for...

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Abstract

The invention discloses a region-based multi-feature fusion high-resolution remote sensing image segmentation method. The method comprises the steps of firstly, performing initial segmentation on an initial high-resolution image; secondly, calculating a texture feature distance, a spectral feature distance and a shape feature distance of any adjacent region in initially segmented regions; and finally, performing region combination based on an RAG (Region Adjacency Graph) and an NNG (Nearest Neighbor Graph). According to the method, a combination rule is established by comprehensively adopting a spectral feature, a texture feature, a shape feature and the like; compared with a rule established by separately adopting a feature, the combination rule better conforms to semantic description of an object, so that the segmentation precision is higher; an adjacency relation of the regions is maintained by jointly adopting two data structures of the RAG and the NNG, so that higher execution efficiency can be obtained by an algorithm; and compared with the prior art, a segmentation result can be obtained more quickly.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a region-based multi-feature fusion high-resolution remote sensing image segmentation method. Background technique [0002] While high spatial resolution remote sensing images bring opportunities for the development of remote sensing technology, they also bring new challenges to the processing of remote sensing data. The accuracy of the method is reduced. Based on this, object-based image analysis (OBIA) has become a new choice for high-resolution remote sensing image processing, and the basis of OBIA is image segmentation. Homogeneous regions, namely objects, are obtained through image segmentation technology. , and then analyze the object as a primitive, which can make full use of the characteristics of the object such as spectrum, texture, shape, etc., which has more advantages than traditional pixel-level algorithms in theory and practice. At present, re...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06K9/62G06T7/40
CPCG06T2207/10032G06F18/22G06F18/253
Inventor 韩玲刘大伟宁晓红刘志恒
Owner CHANGAN UNIV
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