Experimental evaluation of neural, statistical and model-based approaches to FLIR ATR

被引：5

作者：

Li, BX ^{[1
]}

Zheng, QF ^{[1
]}

Der, S ^{[1
]}

Chellappa, R ^{[1
]}

Nasrabadi, NM ^{[1
]}

Chan, LA ^{[1
]}

Wang, LC ^{[1
]}

机构：

[1] Univ Maryland, Ctr Automat Res, College Pk, MD 20742 USA

来源：

AUTOMATIC TARGET RECOGNITION VIII | 1998年 / 3371卷

关键词：

automatic target recognition; performance evaluation;

D O I：

10.1117/12.323856

中图分类号：

V [航空、航天];

学科分类号：

08 ; 0825 ;

摘要：

This paper presents an empirical evaluation of a number of recently developed Automatic Target Recognition algorithms for Forward-Looking InfraRed(FLIR) imagery using a large database of real second-generation FLIR images. The algorithms evaluated are based on convolution neural networks (CNN), principal component analysis (PCA), linear discriminant analysis (LDA), learning vector quantization (LVQ), and modular neural networks (MNN). Two model-based algorithms, using Hausdorff metric based matching and geometric hashing, are also evaluated. A hierarchical pose estimation system using CNN plus either PCA or LDA, developed by the authors, is also evaluated using the same data set.

引用

页码：388 / 397

页数：10