Multi-feature fusion-based polarimetric synthetic aperture radar (SAR) image classification method

Abstract

A multi-feature fusion-based polarimetric SAR image classification method disclosed by the present invention comprises the steps of firstly extracting the polarimetric feature vectors of a to-be-classified polarimetric SAR image to obtain a high-dimension polarimetric feature set; extracting the morphological profile feature vectors of the SPAN processing results of the image to obtain a high-dimension morphological feature set; carrying out the dimension reduction processing of the locality preserving discrimination analysis on the two kinds of high-dimension features separately, and then selecting the image pixel points of the known category labels to form a training sample set, and then selecting the rest pixel points of the whole image as a classification sample set; using a maximum posterior probability-based support vector machine (SVM) to process the two kinds of low-dimension features separately to obtain the category labels and the corresponding posterior probabilities of the pixel points on the respective conditions; adopting a summation criterion or an adaptive weighted summation criterion to combine the posterior probability vectors of each pixel point on the two conditions, and according to the maximum posterior probability principle, obtaining a final classification result of the high-resolution polarimetric SAR image. According to the present invention, the classification accuracy and the efficiency of the resolution polarimetric SAR image are helpful to be improved.

Description

Technical field [0001] The invention belongs to radar image processing and interpretation technology, that is, ground object classification processing is performed on high-resolution polarization synthetic aperture radar images, and specifically relates to a polarization SAR image classification method based on multi-feature fusion. Background technique [0002] In recent years, my country has realized the successful application of synthetic aperture radar (SAR) in various fields. The successful application of SAR relies on effective SAR image processing and interpretation technology, and the premise is SAR image classification technology. Through classification, the ground feature information in SAR images can be effectively obtained, and it can provide help for various applications such as urban planning, crop and forest observation, disaster assessment, and ground target recognition. Therefore, SAR image classification technology occupies a very important position. [0003] The...

AI Technical Summary

Problems solved by technology

Typical dimensionality reduction methods include linear dimensionality reduction methods and manifold learning methods. Linear dimensionality reduction methods are based on the Gaussian hypothesis, but real data are often in complex nonlinear spaces, requiring nonlinear methods to explore potential structures, and The linear dimensionality reduction method does not have the ability to cover the geometry and local distribution structure in the data space, and this information is crucial for classification; the manifold learning method in the nonlinear dimensionality reduction method assumes that the features can be retained at a low In the three-dimensional manifold structure, however, the inherent unsupervised and non-discriminative nature of manifold learning makes it difficult to apply in practice, and the feature vectors of different categories cannot be separated optimally, which is easy to cause misclassification and misclassification; (4) combining The most prominent problem in the two types of features is how to effectively combine different types of features, which requires the so-called information fusion technology

The difficulty of information fusion is that the space of different features is different, and the corresponding kernel functions are also different; the second is that the original feature information may be destroyed, especially when the magnitude of the two features differs greatly; The increase in capacity will easily lead to a decrease in computing efficiency

At present, there is no in-depth study on the information fusion method in the interpretation of polarimetric SAR images

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