Polarized SAR (synthetic aperture radar) image classification method based on depth PCA (principal component analysis) network and SVM (support vector machine)

Abstract

The invention discloses a polarized SAR (synthetic aperture radar) image classification method based on a depth PCA (principal component analysis) network and an SVM (support vector machine) classifier. The polarized SAR image classification method includes filtering a polarized SAR image, extracting a shape feature parameter, a scattering feature parameter, a polarization feature parameter and independent elements of a covariance matrix C, and combing and normalizing into new high-dimensional features serving as data to be processed in a next step; according to actual ground feature flags, randomly selecting 10% of data with flags from each type to serve as training samples; whitening the training samples to serve as input to train a first layer of the network, taking a result as input of a second layer to train the second layer of the network, and performing binaryzation and histogram statistics on an output result; taking output of the depth PCA network as a finally learned feature training SVM classifier; whitening test samples, and inputting the test samples into a trained network framework to predict and calculate accuracy; coloring and displaying a classified image and outputting a final result.

Description

technical field [0001] The invention belongs to the technical field of image processing, and relates to an application in the technical field of polarization synthetic aperture radar image ground object classification, specifically a new polarization SAR based on deep principal component analysis (Principal Component Analysis, PCA) network and SVM The image classification method can be used for ground object classification and target recognition of polarimetric SAR images, and can effectively improve the accuracy rate of polarimetric SAR image classification. Background technique [0002] Synthetic Aperture Radar (SAR) can obtain all-time, all-weather, high-resolution remote sensing images. As an important means of remote sensing image acquisition, it has a wide range of applications. Polarized synthetic aperture radar (polarized SAR) describes the observed land cover and targets by transmitting and receiving polarized radar waves, and can obtain richer target information. I...

AI Technical Summary

Problems solved by technology

There are two defects in the H / α classification: one is that the division of regions is too arbitrary; the other is that the same type of features may be divided into different areas, and at the same time, different types of features may also exist in the same area

[0005] Since a single target decomposition cannot completely describe the scattering mechanism of ground objects, the features formed by it are not enough to characterize the actual ground objects, which leads to poor classification effect of polarimetric SAR images and cannot meet the requirements

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