TY - JOUR
T1 - Deep knowledge integration of heterogeneous features for domain adaptive SAR target recognition
AU - Zhang, Yukun
AU - Guo, Xiansheng
AU - Li, Lin
AU - Ansari, Nirwan
N1 - Funding Information:
This work is supported by the National Natural Science Foundation of China under Grants (No. 62171086 , No. 61771114 ).
Publisher Copyright:
© 2022 Elsevier Ltd
PY - 2022/6
Y1 - 2022/6
N2 - How to integrate various heterogeneous features for better recognition performance is increasingly critical for automatic target recognition. Existing integration methods present the following drawbacks: (1) most feature integration methods ignore the information, both common and discriminate knowledge, among different types of features; (2) most decision integration methods ignore the fact that different knowledge contributes differently; (3) the feature weights of integration model learned in the source domain cannot perform well in the target domain. To tackle these problems, we propose a deep Knowledge Integration framework by combining heterogeneous features for Domain Adaptive synthetic aperture radar (SAR) target recognition (KIDA). In the training phase, we implement deep knowledge integration at both feature and decision levels. At the feature level, to exploit the common and discriminative knowledge, multiple heterogeneous features are projected from the feature space into a unified label space by exploring the shared and specific structures simultaneously. The shared structure integrates common information in different features, while the specific structure reserves discriminative information of each type of feature. At the decision level, to reveal the relative importance of different knowledge, a decision integration strategy with feature weights is adopted in the label space. In the online testing phase, to improve the generalization of the model in dynamical environments, we employ online learning with sequential target domain knowledge to update the feature weights, thus achieving domain adaptation. Extensive experiments on different datasets validate the effectiveness and advantages of the proposed KIDA, especially in noisy environments.
AB - How to integrate various heterogeneous features for better recognition performance is increasingly critical for automatic target recognition. Existing integration methods present the following drawbacks: (1) most feature integration methods ignore the information, both common and discriminate knowledge, among different types of features; (2) most decision integration methods ignore the fact that different knowledge contributes differently; (3) the feature weights of integration model learned in the source domain cannot perform well in the target domain. To tackle these problems, we propose a deep Knowledge Integration framework by combining heterogeneous features for Domain Adaptive synthetic aperture radar (SAR) target recognition (KIDA). In the training phase, we implement deep knowledge integration at both feature and decision levels. At the feature level, to exploit the common and discriminative knowledge, multiple heterogeneous features are projected from the feature space into a unified label space by exploring the shared and specific structures simultaneously. The shared structure integrates common information in different features, while the specific structure reserves discriminative information of each type of feature. At the decision level, to reveal the relative importance of different knowledge, a decision integration strategy with feature weights is adopted in the label space. In the online testing phase, to improve the generalization of the model in dynamical environments, we employ online learning with sequential target domain knowledge to update the feature weights, thus achieving domain adaptation. Extensive experiments on different datasets validate the effectiveness and advantages of the proposed KIDA, especially in noisy environments.
KW - Domain adaptation
KW - Heterogeneous features
KW - Knowledge integration
KW - Online learning
KW - Synthetic aperture radar
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U2 - 10.1016/j.patcog.2022.108590
DO - 10.1016/j.patcog.2022.108590
M3 - Article
AN - SCOPUS:85125535559
SN - 0031-3203
VL - 126
JO - Pattern Recognition
JF - Pattern Recognition
M1 - 108590
ER -