ARTIFICIAL NEURAL NETWORKS FOR AUTOMATIC TARGET RECOGNITION

被引：16

作者：

DANIELL, CE ^{[1
]}

KEMSLEY, DH ^{[1
]}

LINCOLN, WP ^{[1
]}

TACKETT, WA ^{[1
]}

BARAGHIMIAN, GA ^{[1
]}

机构：

[1] HUGHES AIRCRAFT CO,MISSILE SYST GRP,IMAGE GUIDANCE DESIGN LAB,CANOGA PK,CA 91309

来源：

OPTICAL ENGINEERING | 1992年 / 31卷 / 12期

关键词：

AUTOMATIC TARGET RECOGNITION; NEURAL NETWORKS; NEOCOGNITRON; PATTERN RECOGNITION;

D O I：

10.1117/12.60744

中图分类号：

O43 [光学];

学科分类号：

070207 ; 0803 ;

摘要：

The Self Adaptive Hierarchical Target identification and Recognition Neural Network (SAHTIRN(TM)) is a unique and powerful combination of state-of-the-art neural network models for automatic target recognition applications. It is a combination of three models: (1) an early vision segmentor based on the Canny edge detector, (2) a hierarchical feature extraction and pattern recognition system based on a modified Neocognitron architecture, and (3) a pattern classifier based on the back-propagation network. Hughes has extensively tested SAHTIRN(TM) with several ground vehicular targets using terrain board modeled IR imagery under a current neural network program sponsored by the Defense Advanced Research Projects Agency. In addition, extensive testing was conducted using several real IR and handwritten character databases. Hughes has demonstrated successful performance with 91 to 100% probability of correct classification over this wide variety of data. End-to-end system results from these experiments are provided and interim results from each stage of the SAHTIRN(TM) system are discussed.

引用

页码：2521 / 2531

页数：11

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