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1.重庆大学 光电技术及系统教育部重点实验室, 重庆 400044
2.重庆大学 光电工程学院测控技术与仪器专业, 重庆 400044
Received:03 January 2023,
Revised:09 February 2023,
Published:10 September 2023
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刘英旭,蒲春宇,许典坤等.面向高光谱影像场景分类的轻量化深度全局-局部知识蒸馏网络[J].光学精密工程,2023,31(17):2598-2610.
LIU Yingxu,PU Chunyu,XU Diankun,et al.Lightweight deep global-local knowledge distillation network for hyperspectral image scene classification[J].Optics and Precision Engineering,2023,31(17):2598-2610.
刘英旭,蒲春宇,许典坤等.面向高光谱影像场景分类的轻量化深度全局-局部知识蒸馏网络[J].光学精密工程,2023,31(17):2598-2610. DOI: 10.37188/OPE.20233117.2598.
LIU Yingxu,PU Chunyu,XU Diankun,et al.Lightweight deep global-local knowledge distillation network for hyperspectral image scene classification[J].Optics and Precision Engineering,2023,31(17):2598-2610. DOI: 10.37188/OPE.20233117.2598.
针对目标场景复杂的空间布局和高光谱影像固有的空-谱信息冗余等挑战,提出了端到端的轻量化深度全局-局部知识蒸馏(Lightweight Deep Global-Local Knowledge Distillation,LDGLKD)网络。为探索空-谱特征的全局序列属性,教师模型视觉Transformer(Vision Transformer,ViT)被用来指导轻量化学生模型进行高光谱影像场景分类。LDGLKD选择预训练的VGG16作为学生模型来提取局部细节信息,将ViT和VGG16通过知识蒸馏协同训练后,教师模型将所学习到的远程上下文关系向小规模学生模型进行传递。LDGLKD可通过知识蒸馏结合上述两种模型的优点,在欧比特高光谱影像场景分类数据集OHID-SC及公开的高光谱遥感图像数据集HSRS-SC上的最佳分类精度分别达到91.62%和97.96%。实验结果表明:LDGLKD网络具有良好的分类性能。根据欧比特珠海一号卫星提供的遥感数据构建的OHID-SC可以反映详细的地表覆盖情况,并为高光谱场景分类任务提供数据支撑。
To address the challenges of the complex spatial layouts of target scenes and inherent spatial-spectral information redundancy of HSIs, an end-to-end lightweight deep global–local knowledge distillation (LDGLKD) method is proposed herein. To explore the global sequence properties of spatial-spectral features, the vision transformer (ViT) is used as the teacher to guide the lightweight student model for HSI scene classification. In LDGLKD, pre-trained VGG16 is selected as the student model to extract local detail information. After collaborative training of ViT and VGG16 through knowledge distillation, the teacher model transmits the learned long-range contextual information to the small-scale student model. By combining the advantages of the two models through knowledge distillation, the optimal classification accuracy of LDGLKD on the Orbita HSI scene classification dataset (OHID-SC) and hyperspectral remote sensing dataset for scene classification (HSRS) reached 91.62% and 97.96%, respectively. The experimental results revealed that the proposed LDGLKD method presented good classification performance. In addition, the OHID-SC based on the remote sensing data obtained by the Orbita Zhuhai-1 satellite could reflect the detailed information of land cover and provide data support for HSI scene classification.
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