Method for segmentation of high resolution remote sensing image based on veins clustering constrain

A high-resolution, remote sensing image technology, applied in image analysis, image data processing, instruments, etc., can solve problems such as insufficient utilization of texture information, and achieve high segmentation accuracy, high segmentation accuracy, and accurate boundaries.

Inactive Publication Date: 2009-08-19
SHANGHAI JIAO TONG UNIV
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Problems solved by technology

[0006] The purpose of the present invention is to overcome the defect of insufficient use of texture information in the high-resolution remote sensing image segmentation in the prior art, and propose a high-resolution remote sensing image segmentation method based on texture clustering constraints, which can affect the region merging durin

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  • Method for segmentation of high resolution remote sensing image based on veins clustering constrain
  • Method for segmentation of high resolution remote sensing image based on veins clustering constrain
  • Method for segmentation of high resolution remote sensing image based on veins clustering constrain

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Embodiment Construction

[0017] The embodiments of the present invention are described in detail below: the present embodiment is implemented under the premise of the technical solution of the present invention, and detailed implementation and specific operation process are provided, but the protection scope of the present invention is not limited to the following implementation example.

[0018] In this embodiment, the FCM algorithm is used to cluster the regions according to the texture features, and the clustering results are used to assign labels to the regions. Then, a semantically consistent multi-feature distance model is proposed. The distance model comprehensively uses the information of spectrum, texture and shape to measure the homogeneity distance between regions, and adds the texture clustering distance between regions so that the region can be merged according to the direction of texture homogeneity. Finally, the multi-feature distance model is applied to the RAG and NNG graph models, a...

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Abstract

The invention relates to a high-resolution remote sensing image segmentation method based on texture clustering constraint in the field of remote sensing technology, which comprises the concrete steps of: step 1, calculating Gabor energy textures of all regions in an image, carrying out clustering of all the regions by using FCM according to texture distance, and setting a texture clustering tag for each region according to clustering results; step 2, setting up a comprehensive distance space model by using features such as spectrum, texture, shape and the like, and adding texture clustering distance to restrict and lead region merging to be performed along the homogeneous texture direction; and step 3, setting up an RAG model and an NNG model according to the comprehensive distance, and merging the regions according to global optimum. The real regional boundary is obtained by the interaction of the texture clustering and optimal merging sequence during the merging. The method can better segment the texture region in the high-resolution image and improve the overall segmentation accuracy of the image.

Description

technical field [0001] The invention relates to an image segmentation method in the technical field of remote sensing, in particular to a high-resolution remote sensing image segmentation method based on texture clustering constraints. Background technique [0002] With the continuous improvement of satellite spatial resolution, object-oriented image analysis methods have been widely used in the field of remote sensing image processing. Compared with pixel-based classification methods, object-oriented image analysis methods can reduce the impact of noise to a greater extent, extract more features, and are easy to combine with GIS (Geographical Information System, geographic information system). But the key part of the object-oriented image analysis method - image segmentation - has not been well resolved. [0003] According to the feature space, image segmentation methods can be roughly divided into three categories: spectrum-based segmentation methods, texture-based segmen...

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Application Information

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IPC IPC(8): G06T7/00G01S7/48
Inventor 方涛李楠霍宏
Owner SHANGHAI JIAO TONG UNIV
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