Observation vector difference based method for classifying synthetic aperture radar (SAR) image textures

Abstract

The invention discloses an observation vector difference based method for classifying SAR image textures. The method mainly solves the problem of terrain classification of SAR images. The method comprises the steps of (1) randomly selecting r images from a training set for partitioning processing, converting to obtain a column vector difference matrix P; (2) observing the P with an observation matrix to obtain a texture observation vector difference matrix X, and conducting clustering on the X to obtain a texture dictionary D; (3) calculating images of the training set according to Step (2) to obtain an observation vector difference matrix Xtr; (4) projecting the Xtr onto the texture dictionary D to form a training image texture column diagram h; (5) representing images of a test set by a test image texture column diagram he; (6) calculating the distance between the he and the h, and determining the classification to which the he belongs according to the distance; and (7) calculating all test images according to Step (6) to obtain a final classification rate. According to the method, the latest compressed sensing theory is applied, the process is simple, the classification identification rate is high, and the method is applicable to terrain texture classification of SAR images.

Description

technical field [0001] The invention belongs to the technical field of image processing, relates to SAR image classification, and can be applied to the recognition and classification of SAR image ground object targets. Background technique [0002] Synthetic Aperture Radar (SAR) is a high-resolution radar system that can be used in many fields such as military affairs, agriculture, navigation, and geographical surveillance. It has many differences compared with other remote sensing imaging systems and optical imaging systems. In terms of military target recognition, SAR images are widely used in the field of target detection, and the SAR image object classification technology is an extension of the traditional automatic terrain classification technology. However, there are extremely rich information stored in SAR images, and the image feature structure is complex, including natural features such as terrain, vegetation, and hydrology, and artificial features such as houses a...

AI Technical Summary

Problems solved by technology

However, due to the complexity of the SAR image itself, the existing natural image texture processing methods are directly applied to the SAR image, and satisfactory results cannot be obtained.

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