基于跨域小样本学习的SAR图像目标识别方法

doi:10.11896/jsjkx.230800136

Abstract

Abstract: Due to the difficulty in acquiring SAR images and the scarce number of samples available for research,solving the SAR image target recognition problem under few shot conditions has become a community-recognized challenge.With the development of deep learning in the field of computer vision,a variety of few-shot image classification methods have been derived,so a cross-domain few-shot learning paradigm is considered to solve the few-shot SAR image target recognition problem.Concretely,the feature extractors of different domains are first trained inmultiple source domains,while a generalized feature extractor is obtained by knowledge distillation.In this stage,the central kernet alignment method is used to map the extracted features to a higher dimensional space,so as to better distinguish the nonlinear similarity between the original features.Then the target domain image features are extracted by the generalized feature extractor obtained in the previous stage.Finally,a prototype network approach to predict the class of the sample.The experiment proves that the method obtains 88.61% accuracy while reducing the model parameters,which provides a new method for solving the target recognition problem of SAR images with scarce samples.

Key words: Deep learning, Meta learning, Cross domain few shot learning, SAR image target recognition, Knowledge distillation

CLC Number:

TP18

SHI Songhao, WANG Xiaodan, YANG Chunxiao, WANG Yifei. SAR Image Target Recognition Based on Cross Domain Few Shot Learning[J].Computer Science, 2024, 51(6A): 230800136-7.

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SAR Image Target Recognition Based on Cross Domain Few Shot Learning

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