Abstract
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 FLIR images. The algorithms evaluated are based on convolutional neural networks (CNN), principal component analysis (PCA), linear discriminant analysis (LDA), learning vector quantization (LVQ), modular neural networks (MNN), and two model-based algorithms, using Hausdorff metric-based matching and geometric hashing. The evaluation results show that among the neural approaches, the LVQ- and MNN-based algorithms perform the best; the classical LDA and the PCA methods and our implementation of the geometric hashing method ended up in the bottom three, with the CNN- and Hausdorff metric-based methods in the middle. Analyses show that the less-than-desirable performance of the approaches is mainly due to the lack of a good training set.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 5-24 |
| Number of pages | 20 |
| Journal | Computer Vision and Image Understanding |
| Volume | 84 |
| Issue number | 1 |
| DOIs | |
| State | Published - Oct 2001 |
| Externally published | Yes |
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Keywords
- Automatic target recognition
- Performance Evaluation
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition
- Signal Processing
- Electrical and Electronic Engineering
Cite this
Experimental evaluation of FLIR ATR approaches - A comparative study. / Li, Baoxin; Chellappa, R.; Zheng, Q.; Der, S.; Nasrabadi, N.; Chan, L.; Wang, L.
In: Computer Vision and Image Understanding, Vol. 84, No. 1, 10.2001, p. 5-24.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Experimental evaluation of FLIR ATR approaches - A comparative study
AU - Li, Baoxin
AU - Chellappa, R.
AU - Zheng, Q.
AU - Der, S.
AU - Nasrabadi, N.
AU - Chan, L.
AU - Wang, L.
PY - 2001/10
Y1 - 2001/10
N2 - 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 FLIR images. The algorithms evaluated are based on convolutional neural networks (CNN), principal component analysis (PCA), linear discriminant analysis (LDA), learning vector quantization (LVQ), modular neural networks (MNN), and two model-based algorithms, using Hausdorff metric-based matching and geometric hashing. The evaluation results show that among the neural approaches, the LVQ- and MNN-based algorithms perform the best; the classical LDA and the PCA methods and our implementation of the geometric hashing method ended up in the bottom three, with the CNN- and Hausdorff metric-based methods in the middle. Analyses show that the less-than-desirable performance of the approaches is mainly due to the lack of a good training set.
AB - 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 FLIR images. The algorithms evaluated are based on convolutional neural networks (CNN), principal component analysis (PCA), linear discriminant analysis (LDA), learning vector quantization (LVQ), modular neural networks (MNN), and two model-based algorithms, using Hausdorff metric-based matching and geometric hashing. The evaluation results show that among the neural approaches, the LVQ- and MNN-based algorithms perform the best; the classical LDA and the PCA methods and our implementation of the geometric hashing method ended up in the bottom three, with the CNN- and Hausdorff metric-based methods in the middle. Analyses show that the less-than-desirable performance of the approaches is mainly due to the lack of a good training set.
KW - Automatic target recognition
KW - Performance Evaluation
UR - http://www.scopus.com/inward/record.url?scp=0035481110&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0035481110&partnerID=8YFLogxK
U2 - 10.1006/cviu.2001.0938
DO - 10.1006/cviu.2001.0938
M3 - Article
VL - 84
SP - 5
EP - 24
JO - Computer Vision and Image Understanding
JF - Computer Vision and Image Understanding
SN - 1077-3142
IS - 1
ER -