Multi-Aspect SAR Target Recognition Based on Prototypical Network with a Small Number of Training Samples

Zhao, Pengfei; Huang, Lizhen; Xin, Yu; Guo, Jiayi; Pan, Zongxu

doi:10.3390/s21134333

Cited by 8 publications

(1 citation statement)

References 25 publications

(31 reference statements)

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“…The technique proposed for few-shot SAR image classification, known as the spatial transformed prototypical network (ST-PN), has been introduced in the network [45], which incorporates spatial transformations in the prototypical network to enhance its ability to recognize new classes. A few-shot learning framework is based on the prototypical network with a limited number of training samples [46], allowing it to learn a classifier capable of recognizing new classes with only a limited number of examples. A technique utilizing a meta-learning approach with amortized variational inference has been proposed [47], which involves a meta-learning framework to train a model capable of quickly adapting to new classes with only a few examples.…”

Section: B Few-shot Classification In Sar Imagesmentioning

confidence: 99%

Scattering-Point Topology for Few-Shot Ship Classification in SAR Images

Zhang,

Lu,

Qiu

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Synthetic aperture radar (SAR) has emerged as a critical technology for detecting and classifying objects such as ships in challenging environments. However, few-shot learning remains challenging due to the limited availability of labeled SAR data, complex radar backscatter, and variations in imaging parameters. In this paper, we propose a novel network, scattering point topology for few-shot ship classification (SPT-FSC), which addresses these challenges by incorporating scattering characteristics into the network learning process through a scattering point topology (SPT) based on scattering key points. We design a topology encoding branch (TEB) through a series of operations to encode the topological information of scattering points, resulting in a SPT embedding that improves the network's adaptability to the imaging mechanism and reduces imaging variability in SAR images. To effectively fuse the SPT embedding and image features extracted from a convolutional neural network (CNN), we introduce a novel mechanism named reciprocal feature fusion attention (RFFA). Additionally, to address the limited diversity in the training data, we apply fine-tuning based methodologies and construct a fine-grained ship classification dataset by combining the OpenSARShip and FUSAR-Ship datasets. Our comprehensive experiments on these datasets demonstrate the effectiveness of our proposed SPT-FSC method, achieving high accuracy and robustness in few-shot ship classification tasks for SAR images, outperforming existing methods in this domain.

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Section: B Few-shot Classification In Sar Imagesmentioning

confidence: 99%