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A Few-Shot Learning Method for SAR Images Based on Weighted Distance and Feature Fusion

Gao, Fei; Xu, Jingming; Lang, Rongling; Wang, Jun; Hussain, Amir; Zhou, Huiyu


Fei Gao

Jingming Xu

Rongling Lang

Jun Wang

Huiyu Zhou


Convolutional Neural Network (CNN) has been widely applied in the field of synthetic aperture radar (SAR) image recognition. Nevertheless, CNN-based recognition methods usually encounter the problem of poor feature representation ability due to insufficient labeled SAR images. In addition, the large inner-class variety and high cross-class similarity of SAR images pose a challenge for classification. To alleviate the problems mentioned above, we propose a novel few-shot learning (FSL) method for SAR image recognition, which is composed of the multi-feature fusion network (MFFN) and the weighted distance classifier (WDC). The MFFN is utilized to extract input images’ features, and the WDC outputs the classification results based on these features. The MFFN is constructed by adding a multi-scale feature fusion module (MsFFM) and a hand-crafted feature insertion module (HcFIM) to a standard CNN. The feature extraction and representation capability can be enhanced by inserting the traditional hand-crafted features as auxiliary features. With the aid of information from different scales of features, targets of the same class can be more easily aggregated. The weight generation module in WDC is designed to generate category-specific weights for query images. The WDC distributes these weights along the corresponding Euclidean distance to tackle the high cross-class similarity problem. In addition, weight generation loss is proposed to improve recognition performance by guiding the weight generation module. Experimental results on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset and the Vehicle and Aircraft (VA) dataset demonstrate that our proposed method surpasses several typical FSL methods.

Journal Article Type Article
Acceptance Date Sep 9, 2022
Online Publication Date Sep 14, 2022
Publication Date 2022
Deposit Date Sep 28, 2022
Publicly Available Date Sep 28, 2022
Journal Remote Sensing
Publisher MDPI
Peer Reviewed Peer Reviewed
Volume 14
Issue 18
Article Number 4583
Keywords synthetic aperture radar (SAR); convolutional neural network (CNN); radar target recognition; few-shot learning
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