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

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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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 during segmentation through texture clustering preprocessing The order of the region is merged according to the direction of texture homogeneity, and the interaction of the optimal merge sequence and texture clustering is used to find the accurate boundary of the region during the merging process, which improves the accuracy of the segmentation

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