Classification of Targets in SAR Images Using SVM and k-NN Techniques

被引：0
|
作者：
Demirhan, Mahmut Esat [1 ,2 ]
Salor, Ozgul [1 ]
机构：
[1] Gazi Univ, Elekt Elekt Muhendisligi Bolumu, Ankara, Turkey
[2] TUBITAK Turkiye Bilimsel & Teknol Arastirma Kurum, Ankara, Turkey
来源：
2016 24TH SIGNAL PROCESSING AND COMMUNICATION APPLICATION CONFERENCE (SIU) | 2016年
关键词：
Feature extraction; k-Nearest Neighbors (k-NN); modified radial function (MRF); object classification; Synthetic Aperture Radar (SAR); support vector machines (SVM);
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
In this paper, a method developed for classification of various military target types acquired from Synthetic Aperture Radar (SAR) images is described. For classification, first images are enhanced and segmentation is performed. Then, in the feature extraction step, the use of Modified Radial Function - (MRF) based features is proposed, which had not been used in previously for SAR-based classification studies in the literature. In addition to MRF, the mean of the segmented image and ellipse axis rate are used as features to increase the classification accuracy. A classification accuracy of 93.34% has been achieved by using Support Vector Machines (SVM) and k-Nearest Neighbors (k-NN) classifiers.
引用
收藏
页码：1581 / 1584
页数：4
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