Remote sensing image processing method combined with shape self-adaption neighborhood and texture feature extraction

Abstract

The invention discloses a remote sensing image processing method combined with the shape self-adaption neighborhood and the texture feature extraction for image preprocessing. The method includes subjecting compressed image to a gray level co-occurrence matrix calculation; subjecting the generated gray level co-occurrence matrix to S coefficient modification of an SAN (Storage Area Networking) irregular object window to obtain a regular matrix; calculating a new co-occurrence matrix according to the modified regular matrix and selecting texture descriptors with obvious feature and low correlation; extracting texture feature map in the SAN irregular images; and calculating to obtain accurate images with combination feature which is overall comprehensive feature of neighborhood. According to the method, the overall classification accuracy based on a shape self-adaption neighborhood method can be improved by 4%. The method can not only extract the texture feature in the SAN irregular images of remote sensing images completely, but also process the extraction of mixed pixel feature of the fuzzy edge of earth surface objects, and is applicable to texture extraction of earth surface objects in natural states.

Description

technical field [0001] The invention relates to the field of remote sensing image processing, in particular to a remote sensing image processing method combining shape adaptive neighborhood and texture feature extraction. Background technique [0002] With the development of aerospace and aviation technology, the acquisition of multi-source and massive remote sensing data makes it urgent to study the automatic classification of remote sensing images. However, the accuracy of image classification based solely on spectral features is not convincing, and has always been inferior to the recognition accuracy of visual interpretation, so it has not been put into practical applications. Based on the theory of human visual cognition, people add shape features to improve its classification effect, such as: object-oriented, shape adaptive neighborhood (SAN) and other methods to extract features and assist classification. Although it has improved, its classification accuracy is still n...

AI Technical Summary

Problems solved by technology

[0005] Aiming at the deficiencies of the prior art, the present invention proposes a remote sensing image processing method combining shape-adaptive neighborhood and texture feature extraction to ensure that texture features are extracted from irregular SAN shape feature objects, which can solve the problem of texture feature boundary blur problem, and can alleviate the problem of difficult determination of the texture information extraction window, and satisfy the probability distribution characteristics required by the algorithm itself

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