An Efficient Two-Dimensional Subclass Discriminant Analysis Approach for SAR Image Recognition

ZHANG Jing; WANG Guo-hong; YANG Zhi-yong; BR

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An Efficient Two-Dimensional Subclass Discriminant Analysis Approach for SAR Image Recognition

更新时间：2025-07-16

- An Efficient Two-Dimensional Subclass Discriminant Analysis Approach for SAR Image Recognition
- Acta Electronica Sinica Vol. 38, Issue 4, Pages: 798-0803(2010)
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- Published：2010
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ZHANG Jing, WANG Guo-hong, YANG Zhi-yong, et al. An Efficient Two-Dimensional Subclass Discriminant Analysis Approach for SAR Image Recognition[J]. Acta Electronica Sinica, 2010, 38(4): 798-0803.
DOI：

ZHANG Jing, WANG Guo-hong, YANG Zhi-yong, et al. An Efficient Two-Dimensional Subclass Discriminant Analysis Approach for SAR Image Recognition[J]. Acta Electronica Sinica, 2010, 38(4): 798-0803. DOI：

摘要

<FONT face=Verdana>本文在分析传统二维鉴别分析方法局限性的基础上，提出了一种基于二维子分类鉴别分析的合成孔径雷达图像识别方法。该方法首先对SAR图像进行图像预处理，然后利用图像欧氏距离对每类目标进行子类划分，并由图像的行信息和列信息提出了两种二维子分类鉴别分析方法，最后利用最近邻分类器对提取的特征投影矩阵进行分类识别。本文利用美国实测的MSTAR数据对算法进行了仿真验证，实验结果表明了本文方法的正确性和有效性。

Abstract

<FONT face=Verdana>By analyzing the limitation of the traditional two-dimensional LDA

a new SAR image recognition algorithm based on two-dimensional subclass discriminant analysis is proposed in this paper. First

image preprocessing of SAR image is performed

which includes image aligning and power transformation. Then image Euclidean distance NN-Clustering is proposed to divide the datasets per class into multi-subclasses

and feature matrix is extracted by two new methods

i.e. 2DSDA and Alternative-2DSDA. Finally

a nearest neighbor classifier is employed to classify the extracted features. Experimental results with MSTAR dataset verify the correctness and effectiveness of the proposed method.

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