Polarized SAR Image Classification Method Based on Improved Neighbor Propagation Clustering

Abstract

The invention discloses a polarized SAR (synthetic aperture radar) image classification method based on improved affinity propagation clustering. The problem of low classification accuracy in the existing unsupervised polarized SAR classification method is mainly solved. The method comprises the implementation steps of: carrying out four component decompositions on each pixel point, extracting four scattering powers of each pixel point; dividing an image according to the obtained scattering powers to obtain four classes; equally dividing each obtained class into 20 small classes; clustering the 20 small classes in each class by the improved affinity propagation clustering to obtain the pre-classification result of the image; and finally, carrying out iterative classification on a pre-classified image by a Wishart classifier to obtain the final classification result. Compared with the classical classification method, for the method disclosed by the invention, the division on a polarized SAR image is stricter; the classification effect is better; the computation complexity is small; and the polarized SAR image classification method can be used for carrying out terrain classification and target identification on the polarized SAR image.

Description

technical field [0001] The invention belongs to the technical field of image processing, and relates to the application in the field of classification of polarimetric SAR images, in particular to a method for classifying polarimetric SAR images based on the nearest neighbor propagation clustering algorithm, which can be used for the classification of polarimetric SAR images classification and object recognition. Background technique [0002] Polarization SAR radar can obtain richer target information, and has a wide range of research and application values ​​in agriculture, forestry, military, geology, hydrology and oceans, such as the identification of ground object types, crop growth monitoring, yield evaluation, Object classification, sea ice monitoring, land subsidence monitoring, target detection and marine pollution detection, etc. The purpose of polarimetric SAR image classification is to use the polarization measurement data obtained by airborne or spaceborne polari...

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 when several different features coexist in the same region, they cannot be effectively distinguished

However, when the algorithm is applied to the field of image segmentation, the amount of calculation and storage is unbearable, which seriously hinders the performance of the algorithm.

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