A statistical distribution texton feature for synthetic aperture radar image classification

Chu HE; Ya-ping YE; Ling TIAN; Guo-peng YANG; Dong CHEN

doi:10.1631/FITEE.1601051

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A statistical distribution texton feature for synthetic aperture radar image classification

Regular Papers | Updated：2022-05-19

- A statistical distribution texton feature for synthetic aperture radar image classification
- 用于SAR图像分类的统计分布基元特征
- Frontiers of Information Technology & Electronic Engineering Vol. 18, Issue 10, Pages: 1614-1623(2017)
- Affiliations：
  
  School of Electronic Information, Wuhan University, Wuhan 430072, China
  State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430072, China
  School of Electronic Science and Engineering, National University of Defense Technology, Changsha 410073, China
  Hubei P&T Plan-Design Co., Ltd., Wuhan 430023, China
- Author bio：
  
  Ling TIAN, E-mail: lingtianwhu@126.com
- Funds：
- DOI：10.1631/FITEE.1601051
  CLC： TP753
- Received：05 March 2016，
  
  Revised：2017-10-10，
  
  Published：2017-10
- Accepted：
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Chu HE, Ya-ping YE, Ling TIAN, et al. A statistical distribution texton feature for synthetic aperture radar image classification[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(10): 1614-1623.
DOI：

Chu HE, Ya-ping YE, Ling TIAN, et al. A statistical distribution texton feature for synthetic aperture radar image classification[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(10): 1614-1623. DOI： 10.1631/FITEE.1601051.

摘要

本文提出了一种新颖的用于合成孔径雷达图像分类的统计分布基元（S基元）特征。受传统基元特征、纹理分析框架及SAR图像中统计分布重要性的启发，本文基于统计分布的参数估计可以替代传统SAR图像纹理分析中滤波器操作这一想法提出了S基元特征。在S基元特征的提取过程中，本文采用了一些策略，如预处理、空间网格化、参数估计、基元集群、直方图统计。在TerraSAR数据上的实验结果验证了本文S基元特征的有效性。

Abstract

We propose a novel statistical distribution texton (s-texton) feature for synthetic aperture radar (SAR) image classification. Motivated by the traditional texton feature

the framework of texture analysis

and the importance of statistical distribution in SAR images

the s-texton feature is developed based on the idea that parameter estimation of the statistical distribution can replace the filtering operation in the traditional texture analysis of SAR images. In the process of extracting the s-texton feature

several strategies are adopted

including pre-processing

spatial gridding

parameter estimation

texton clustering

and histogram statistics. Experimental results on TerraSAR data demonstrate the effectiveness of the proposed s-texton feature.

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references

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