Polarimetric SAR classification method on basis of NSCT and discriminative dictionary learning

Abstract

The invention discloses a polarimetric SAR classification method on the basis of NSCT and discriminative dictionary learning and mainly solves the problems of low classification accuracy and low classification speed of an existing polarimetric SAR image classification method. The polarimetric SAR classification method comprises the following implementing steps: 1, acquiring a coherence matrix of a polarimetric SAR image to be classified and carrying out Lee filtering on the coherence matrix to obtain the de-noised coherence matrix; 2, carrying out Cloude decomposition on the de-noised coherence matrix and using three non-negative feature values of decomposition values and a scattering angle as classification features; 3, carrying out three-layer NSCT on the classification features and using a transformed low-frequency coefficient as a transform domain classification feature; 4, using the transform domain classification feature and combining a discriminative dictionary learning model to train a dictionary and a classifier; 5, using the dictionary and the classifier, which are obtained by training, to classify a test sample so as to obtain a classification result. The polarimetric SAR classification method improves classification accuracy and increases a classification speed and is suitable for image processing.

Description

technical field [0001] The invention belongs to the technical field of image processing, in particular to a SAR image classification method, which can be used in the field of target recognition. Background technique [0002] Synthetic Aperture Radar (SAR) is popular because of its powerful advantages of all-day, all-weather, and high-resolution. Compared with the traditional single-polarization SAR, the polarization SAR measures the target in all polarizations and can obtain richer target information, so it has attracted much attention in recent years. [0003] The images obtained by polarimetric SAR are called polarimetric SAR images. Polarimetric SAR image classification is an important research content in the process of polarimetric SAR image interpretation. Its purpose is to use the polarimetric measurement data acquired by the polarimetric SAR sensor to determine the category of each unit in the polarimetric SAR image. The results of polarimetric SAR image classificat...

AI Technical Summary

Problems solved by technology

First of all, the SRC method classifies data based on the minimum criterion of reconstruction error, but in fact there is a difference between data reconstruction and data classification. If only reconstruction error is considered without classification error, it will affect the data Classification effect; Secondly, since the SRC classifier is based on the minimum criterion of reconstruction error, each sample needs to perform multiple reconstruction error calculations, which will have an impact on the classification speed; Finally, polarimetric SAR image data due to its imaging principle The particularity makes it have strong multiplicative noise, so if it is directly classified in the original feature domain of polarimetric SAR image data, it will be disturbed by noise and affect its classification effect

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