Polarized SAR (synthetic aperture radar) image object classifying method based on multi-quantum ridgelet representation

Abstract

The invention discloses a polarized SAR image object classifying method based on multi-quantum ridgelet representation. The polarized SAR image object classifying method based on multi-quantum ridgelet representation solves the problem of insufficient feature representation, low classification precision and high time complexity of the prior art. The method is implemented through the steps of, firstly, extracting the image features of a polarized SAR image; secondly, combining the features into a feature matrix and performing normalization; thirdly, selecting a training data set and a testing data set from the feature matrix; fourthly, training the training data set through a double-quantum ridgelet network; fifthly, training and classifying the training data set through an artificial neural network (NN) classifier; sixthly, classifying the test data set through a trained classifier. By means of the multi-quantum ridgelet neural network, the polarized SAR image object classifying method based on multi-quantum ridgelet representation is flexible in structure and improves the presentation ability of the image features of the polarized SAR image, thereby effectively improving the classification precision of the SAR image, reducing time complexity and being applicable to classification of complex images.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a multi-feature and multi-category polarization SAR image ground object classification method, which can be used in the fields of target recognition, target tracking and the like. Background technique [0002] Polarimetric SAR has become one of the most advanced sensors in the field of remote sensing, and polarimetric SAR image classification is an important research technology for SAR image interpretation. Polarimetric SAR can describe the target more comprehensively, and its measurement data contains more abundant target information. Therefore, polarimetric SAR has very obvious advantages in target detection, classification and parameter inversion. The purpose of polarimetric SAR image classification is to use the polarization measurement data obtained by airborne or spaceborne polarimetric SAR sensors to determine the category to which each pixel belongs. ...

AI Technical Summary

Problems solved by technology

One defect of H / α classification is that the division of regions is too arbitrary. When the same class of data is distributed on the boundary of two or more classes, the performance of the classifier will deteriorate. Another shortcoming is that when the data in the same area When several different features coexist, it will not be able to effectively distinguish

However, the disadvantages of this algorithm are that it cannot maintain the polarization scattering characteristics of various types very well, and because the number of classification categories is fixed at 8, it cannot adapt to the classification of ground objects with different numbers of categories, so for categories with more than 8 categories or For data with less than 8 categories, the classification effect of the algorithm will be affected

Although the above methods have improved the image classification effect, there are still problems such as inability to adapt to different band polarization data and different categories of object classification, poor image detail information retention, low classification accuracy, and high time complexity.

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