A class-adaptive polarization sar classification method

Abstract

The invention discloses a class-adaptive polarimetric SAR (synthetic aperture radar) classification method and belongs to the technical field of image processing. The classification method comprises the following steps: performing Freeman decomposition on input data to obtain scattered power matrixes Ps, Pd and Pv; initially dividing polarimetric SAR image data into three classes according to the values of the Ps, Pd and Pv; calculating a co-polarization ratio R of each pixel and selecting two different thresholds for further dividing each class into three classes; calculating a self-polarization parameter delta of each pixel point in each class and subdividing each class into N classes according to the value of the self-polarization parameter delta; calculating class differences for subdivision results to obtain a dissimilarity matrix R[D], rearranging the dissimilarity matrix R[D] by visual assessment of cluster tendency to obtain a new matrix R[D]<1>; transforming the new matrix R[D]<1> into a dissimilarity image Im and performing dark block extraction on the dissimilarity image Im to obtain a class number and a cluster center; classifying all input polarimetric SAR data by a complex Wishart iteration method and coloring to obtain a final color classification result graph. According to the method, split areas used for identifying a polarimetric SAR image target are good in consistency and retained information is complete.

Description

technical field [0001] The invention belongs to the field of image processing, specifically aims at classification of polarimetric SAR images, and can be applied to target detection and target recognition of polarimetric SAR. Background technique [0002] Compared with the traditional synthetic aperture radar SAR system, polarimetric SAR can obtain richer target information and greatly improve the ability to identify ground objects. Therefore, polarimetric SAR has become the development trend of SAR, and has a wide range of applications in military fields, geology and resource exploration, topographic mapping and mapping, marine applications and research, water resources applications, agriculture and forestry applications, etc. Among them, object classification is an important content of polarimetric SAR image interpretation. The current classic classification methods are: [0003] In 1997, the classification method based on H / α polarization decomposition proposed by Cloud...

AI Technical Summary

Problems solved by technology

[0004] In 1999, Lee et al proposed the H / α-Wishart classification method based on H / α target decomposition and Wishart classifier, see LeeJS, GrunesMR, AinsworthTL, et al. 1999, 37(5): 2249-2258. This method is to increase the Wishart iteration on the basis of the original H / α classification, mainly to use the Wishart classifier to re-classify each pixel for the 8 categories after the H / α classification, so that Effectively improves the accuracy of classification, but cannot maintain the polarization scattering characteristics of various types very well

[0005] In 2011, Xidian University Shuang Wang et al. used the three kinds of scattering power obtained by Freeman decomposition to divide the initial categories of images, and used the same polarization ratio to divide the initial categories more carefully. Finally, based on the initial category division On the other hand, complex Wishart iterations are performed on the entire image to further improve the classification accuracy. See the literature ShuangWang, JingjingPei, KunLiu, et al. Understand that it has high classification accuracy, but the algorithm still has certain limitations. The number of classification categories of the algorithm is fixed, generally 9 categories, so for data with more than 9 categories or less than 9 categories , the classification effect of the algorithm will be affected

Images