Polarization-texture characteristic and DPL-based polarimetric SAR image classification method

Abstract

The invention discloses a polarization-texture characteristic and DPL (Dictionary Pair Learning)-based polarimetric SAR (Synthetic Aperture Radar) image classification method, and mainly solves the problems of long calculation time and low classification efficiency caused by non-comprehensive image information and poor dictionary distinguishing performance in a polarimetric SAR image classification process in the prior art. The method comprises the following specific steps of (1) reading polarimetric SAR images; (2) performing filtering; (3) creating a sample set; (4) performing sparse coding; (5) creating a neighborhood characteristic sample matrix; (6) normalizing the neighborhood characteristic sample matrix; (7) selecting a training sample and a test sample; (8) training a synthetic dictionary and an analytic dictionary; (9) testing the synthetic dictionary and the analytic dictionary; (10) performing coloring; and (11) outputting a classification result graph. The method has the advantages of high classification correctness and high classification efficiency of the polarimetric SAR images.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a polarimetric synthetic aperture radar SAR (Synthetic Aperture Radar) image classification method based on polarization-texture features and dual dictionary learning DPL (Dictionary Pair Learning) in the technical field of target recognition . The present invention can be used for ground object classification of polarimetric SAR images. Background technique [0002] Synthetic aperture radar is a high-resolution imaging radar. Because microwaves have penetrating properties and are not affected by light intensity, synthetic aperture radars have all-day and all-weather working capabilities. With the development of technology, synthetic aperture radar is gradually developing in the direction of high resolution, multi-polarization and multi-channel. Compared with traditional SAR images, polarimetric SAR can provide more abundant target information, which is benefici...

AI Technical Summary

Problems solved by technology

Although this method combines the histogram segmentation of the H and α parameters to obtain the partition threshold, there are still shortcomings in that the method is computationally intensive, time-consuming, and the implementation process is complicated.

Although this method improves the traditional classification method based on H / α polarization decomposition, it still has the disadvantage that these two features are not enough to represent all the polarization information, so there are still many area division errors. Considering the texture features of polarimetric SAR images, resulting in more noise points in the region and poor regional consistency

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