Cost-Sensitive Awareness-Based SAR Automatic Target Recognition for Imbalanced Data

With the maturity of synthetic aperture radar (SAR) technology, the problem of imbalanced data has gradually emerged. This problem makes it difficult for the automatic target recognition (ATR) model to properly learn the classification boundaries of majority and minority category target samples. In...

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Veröffentlicht in:IEEE transactions on geoscience and remote sensing Jg. 60; S. 1 - 16
Hauptverfasser: Cao, Changjie, Cui, Zongyong, Wang, Liying, Wang, Jielei, Cao, Zongjie, Yang, Jianyu
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Automatic target recognition
Automatic target recognition (ATR)
Boundaries
Classification
cost-sensitive awareness
Data models
Datasets
Feature extraction
imbalanced data
Methods
Oversampling
SAR (radar)
Sensors
Support vector machines
Synthetic aperture radar
synthetic aperture radar (SAR)
Target acquisition
Target recognition
Training
ISSN:0196-2892, 1558-0644
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Zusammenfassung:With the maturity of synthetic aperture radar (SAR) technology, the problem of imbalanced data has gradually emerged. This problem makes it difficult for the automatic target recognition (ATR) model to properly learn the classification boundaries of majority and minority category target samples. In this article, we propose an ATR model with new architecture, called the cost-sensitive awareness-based automatic target recognition (CA-ATR) model, which provides an effective way of solving the problem of imbalanced data. Aimed at the two issues caused by imbalanced data on ATR models, the proposed method solves the problems from both the data and algorithm levels. At the data level, CA-ATR avoids adverse correlations among the target samples through different oversampling methods. By making the ATR model cost-sensitive, the proposed method also avoids the empirical risk preference of the ATR model for majority category target samples at the algorithm-level. At the same time, CA-ATR can autonomously learn different cost-sensitive awareness from different imbalanced data sets. The awareness enables the ATR model to more accurately learn the classification boundaries between target samples that belong in different categories. Several experimental results show the superiority of the proposed approach based on the Moving and Stationary Target Acquisition and Recognition (MSTAR) data set. Compared with other imbalanced learning methods, the proposed method is able to solve different types of imbalanced data problems.
Bibliographie:ObjectType-Article-1
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ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2021.3068447