Exemplar Selection Based on Maximizing Non-overlapping Volume in SAR Target Incremental Recognition

LI Bin; CUI Zongyong; WANG Haohan; ZHOU Zheng; TIAN Yu; CAO Zongjie

doi:10.11999/JEIT240217

Volume 46 Issue 10

Oct. 2024

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Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(10): 3918-3927

LI Bin, CUI Zongyong, WANG Haohan, ZHOU Zheng, TIAN Yu, CAO Zongjie. Exemplar Selection Based on Maximizing Non-overlapping Volume in SAR Target Incremental Recognition[J]. Journal of Electronics & Information Technology, 2024, 46(10): 3918-3927. doi: 10.11999/JEIT240217

Citation:

LI Bin, CUI Zongyong, WANG Haohan, ZHOU Zheng, TIAN Yu, CAO Zongjie. Exemplar Selection Based on Maximizing Non-overlapping Volume in SAR Target Incremental Recognition[J]. Journal of Electronics & Information Technology, 2024, 46(10): 3918-3927. doi: 10.11999/JEIT240217

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Exemplar Selection Based on Maximizing Non-overlapping Volume in SAR Target Incremental Recognition

doi: 10.11999/JEIT240217

School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China

Funds: The National Natural Science Foundation of China (62271116)

Received Date: 2024-03-28
Rev Recd Date: 2024-08-21

Available Online: 2024-08-30

Publish Date: 2024-10-30

Abstract

Abstract

To ensure the Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) system can quickly adapt to new application environments, it must possess the ability to rapidly learn new classes. Currently, SAR ATR systems require repetitive training of all old class samples when learning new classes, leading to significant waste of storage resources and preventing the recognition model from updating quickly. Preserving a small number of old class examples for subsequent incremental training is crucial for model incremental recognition. To address this issue, Exemplar Selection based on Maximizing Non-overlapping Volume (ESMNV) is proposed in this paper, an exemplar selection algorithm that emphasizes the non-overlapping volume of the distribution. ESMNV transforms the exemplar selection problem for each known class into an asymptotic growth problem of the Non-overlapping volume of the distribution, aiming to maximize the Non-overlapping volume of the distribution of the selected exemplars. ESMNV utilizes the similarity between distributions to represent differences in volume. Firstly, ESMNV uses a kernel function to map the distribution of the target class into a Reconstructed Kernel Hilbert Space (RKHS) and employs higher-order moments to represent the distribution. Then, it uses the Maximum Mean Discrepancy (MMD) to compute the difference between the distribution of the target class and the selected exemplars. Combined with a greedy algorithm, ESMNV progressively selects exemplars that minimize the difference in distribution between the selected exemplars and the target class, ensuring the maximum Non-overlapping volume of the selected exemplars with a limited number.
- SAR target incremental recognition,
- Exemplar selection,
- Non-overlapping volume,
- Maximum Mean Discrepancy (MMD),
- Greedy algorithm.

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References(19)

References

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