液晶与显示2025，Vol.40Issue(4)：630-641,12.DOI:10.37188/CJLCD.2024-0254
结合高效注意力机制的神经架构搜索高光谱图像分类
Neural architecture search combined with efficient attention for hyperspectral image classification
摘要
Abstract
Due to the significant differences in the number of bands,spectral range and spatial resolution of different hyperspectral image datasets,the optimal network structures applicable to different hyperspectral image datasets also differ.In addition,manually designed deep learning networks need to tune a large number of hyperparameters,which undoubtedly poses a serious challenge to designing a generalized classification model applicable to various HSI datasets.Therefore,an efficient attention neural architecture search algorithm is proposed to realize the automatic design of deep learning networks.Firstly,in order to construct an efficient search process,a model is constructed based on the search of microable network architecture,which can effectively improve the search speed of hyperparametric networks.Then,in order to achieve high-precision classification results,a novel modular search space is designed.Finally,considering the misclassification problem of small samples in hyperspectral datasets,Poly loss function is used to increase the loss weights of a few categories,so as to improve the model's ability to recognize these categories.Experimental results on publicly available hyperspectral datasets show that the overall classification accuracy of the proposed method reaches 99.50%and 97.81%,respectively.The proposed method explores the application of neural architecture search in hyperspectral classification tasks,improving classification accuracy and algorithm design efficiency.关键词
高光谱图像/图像分类/神经架构搜索/注意力机制Key words
hyperspectral image/image classification/neural architecture search/attention mechanism分类
信息技术与安全科学引用本文复制引用
陈海松,张康,吕浩然,王爱丽,吴海滨..结合高效注意力机制的神经架构搜索高光谱图像分类[J].液晶与显示,2025,40(4):630-641,12.基金项目
黑龙江省重点研发计划(No.JD2023SJ19) （No.JD2023SJ19）
黑龙江省自然科学基金(No.LH2023F034) （No.LH2023F034）
深圳职业大学校级科研项目(No.6025310007K)Supported by the Key Research and Development Plan Project of Heilongjiang(No.JD2023SJ19) （No.6025310007K）
Natural Science Foundation of Heilongjiang Province(No.LH2023F034) （No.LH2023F034）
Shenzhen Polytechnic University Research Fund(No.6025310007K) （No.6025310007K）
