[1]陈春林,张礼,刘学军. 针对SAR图像的树形稀疏表示结构识别算法研究[J].计算机技术与发展,2017,27(08):20-24.
　CHEN Chun-lin,ZHANG Li,LIU Xue-jun. Investigation on Identification Algorithm of Tree-structure Sparse Representation for SAR Target[J].,2017,27(08):20-24.
点击复制

 针对SAR图像的树形稀疏表示结构识别算法研究()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
27
期数:
2017年08期
页码:
20-24
栏目:
智能、算法、系统工程
出版日期:
2017-08-10

文章信息/Info

Title:
 Investigation on Identification Algorithm of Tree-structure Sparse Representation for SAR Target
文章编号:
1673-629X（2017)08-0020-05
作者:
 陈春林; 张礼; 刘学军
 南京航空航天大学 计算机科学与技术学院
Author(s):
 CHEN Chun-lin; ZHANG Li; LIU Xue-jun
关键词:
 ; SAR目标识别; 型号识别; 树形信息字典; 稀疏表示; 字典学习
Keywords:
 ; SAR automatic target recognition; series recognition; tree-structure information dictionary; sparse representation; dictionary learning
分类号:
TP391
文献标志码:
A
摘要:
 为了提高SAR图像的目标识别能力,在一般稀疏表示方法的基础上,提出了一种基于树形稀疏表示结构识别算法-稀疏表示树,以提高目标型号的识别准确率.稀疏表示树是由多个节点组成的树形分类器,在每个节点上设计针对该节点设计的稀疏表示字典和分类器.在单个节点上利用稀疏表示算法求解未知样本的特征向量,并按照重构误差最小原则实现目标型号识别.稀疏表示树方法根据父节点识别结果,将稀疏表示结果相似的样本型号作为子集传递到子节点,并设计新的字典和分类器进行识别.在MSTAR SAR图像数据集上的实测结果表明,所构建的稀疏表示树与数据集数据分布情况一致,并且将目标型号识别率提高至84%,与传统的稀疏表示分类器方法相比,在不增加太多时间开销的条件下可有效提高目标型号的识别准确率.
Abstract:
 In order to improve the ability of identifying SAR target series with sparse representation,a tree-structure sparse coding recognition algorithm is proposed,which is employed to lift the recognition accuracy of target models.The sparse representation tree is a tree-like classifier composed of multiple nodes,each of which has a sparse representation dictionary and a classifier for the node.The sparse representation algorithm is used to solve the eigenvector of unknown sample on a single node,realizing the target type identification according to the minimum principle of reconstruction error.The root node is employed to direct input SAR images with similar sparse results to children nodes,which have more specialized dictionaries and classifiers to identify these target series.Experiments on MSTAR target dataset show that it is suitable for the sample distribution and has improved target recognition rate up to 84%,and that compared with the traditional sparse coding method,it has got effective improvement on the target series recognition accuracy without more time expenditure.

相似文献/References:

[1]张志宏,吴庆波,邵立松,等.基于飞腾平台TOE协议栈的设计与实现[J].计算机技术与发展,2014,24(07):1.
　ZHANG Zhi-hong,WU Qing-bo,SHAO Li-song,et al. Design and Implementation of TCP/IP Offload Engine Protocol Stack Based on FT Platform[J].,2014,24(08):1.
[2]梁文快,李毅. 改进的基因表达算法对航班优化排序问题研究[J].计算机技术与发展,2014,24(07):5.
　LIANG Wen-kuai,LI Yi. Research on Optimization of Flight Scheduling Problem Based on Improved Gene Expression Algorithm[J].,2014,24(08):5.
[3]黄静,王枫,谢志新,等. EAST文档管理系统的设计与实现[J].计算机技术与发展,2014,24(07):13.
　HUANG Jing,WANG Feng,XIE Zhi-xin,et al. Design and Implementation of EAST Document Management System[J].,2014,24(08):13.
[4]侯善江[],张代远[][][]. 基于样条权函数神经网络P2P流量识别方法[J].计算机技术与发展,2014,24(07):21.
　HOU Shan-jiang[],ZHANG Dai-yuan[][][]. P2P Traffic Identification Based on Spline Weight Function Neural Network[J].,2014,24(08):21.
[5]李璨,耿国华,李康,等. 一种基于三维模型的文物碎片线图生成方法[J].计算机技术与发展,2014,24(07):25.
　LI Can,GENG Guo-hua,LI Kang,et al. A Method of Obtaining Cultural Debris’ s Line Chart Based on Three-dimensional Model[J].,2014,24(08):25.
[6]翁鹤,皮德常. 混沌RBF神经网络异常检测算法[J].计算机技术与发展,2014,24(07):29.
　WENG He,PI De-chang. Chaotic RBF Neural Network Anomaly Detection Algorithm[J].,2014,24(08):29.
[7]刘茜[],荆晓远[],李文倩[],等. 基于流形学习的正交稀疏保留投影[J].计算机技术与发展,2014,24(07):34.
　LIU Qian[],JING Xiao-yuan[,LI Wen-qian[],et al. Orthogonal Sparsity Preserving Projections Based on Manifold Learning[J].,2014,24(08):34.
[8]尚福华,李想,巩淼. 基于模糊框架-产生式知识表示及推理研究[J].计算机技术与发展,2014,24(07):38.
　SHANG Fu-hua,LI Xiang,GONG Miao. Research on Knowledge Representation and Inference Based on Fuzzy Framework-production[J].,2014,24(08):38.
[9]叶偲,李良福,肖樟树. 一种去除运动目标重影的图像镶嵌方法研究[J].计算机技术与发展,2014,24(07):43.
　YE Si,LI Liang-fu,XIAO Zhang-shu. Research of an Image Mosaic Method for Removing Ghost of Moving Targets[J].,2014,24(08):43.
[10]余松平[][],蔡志平[],吴建进[],等. GSM-R信令监测选择录音系统设计与实现[J].计算机技术与发展,2014,24(07):47.
　YU Song-ping[][],CAI Zhi-ping[] WU Jian-jin[],GU Feng-zhi[]. Design and Implementation of an Optional Voice Recording System Based on GSM-R Signaling Monitoring[J].,2014,24(08):47.

更新日期/Last Update: 2017-09-20