Detection and classification of MSTAR objects via morphological shared-weight neural networks

被引：9

作者：

Theera-Umpon, N ^{[1
]}

Khabou, MA ^{[1
]}

Gader, PD ^{[1
]}

Keller, JM ^{[1
]}

Shi, HC ^{[1
]}

Li, HZ ^{[1
]}

机构：

[1] Univ Missouri, Dept Comp Sci & Comp Engn, Columbia, MO 65211 USA

来源：

ALGORITHMS FOR SYNTHETIC APERTURE RADAR IMAGERY V | 1998年 / 3370卷

关键词：

morphological shared-weight neural networks; ATR; SAR; MSTAR;

D O I：

10.1117/12.321856

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 we describe the application of morphological shared-weight neural networks (MSNN) to the problems of classification and detection of vehicles in synthetic aperture radar (SAR). Classification experiments were carried out with SAR images of T72 tanks and armored personnel carriers (APC). A correct classification rate of more than 98% was achieved on a testing data set. Detection experiments were carried out with T72 tanks embedded in SAR images of clutter scenes. A near perfect detection rate and a low false alarm rate were achieved. The data used in the experiments was the standard training and testing MSTAR data set collected by Sandia National Laboratory.

引用

页码：530 / 540

页数：11