子空间与存储体的高光谱图像跨域小样本分类  

Subspace andmemory bank for cross-domain few-shot classification of hyperspectral images

作　　者：慕彩红 张富贵 闫香蓉 刘逸[2] MU Caihong;ZHANG Fugui;YAN Xiangrong;LIU Yi(School of Artificial Intelligence,Xidian University,Xi’an 710071,China;School of Electronic Engineering,Xidian University,Xi’an 710071,China)

机构地区：[1]西安电子科技大学人工智能学院,陕西西安710071 [2]西安电子科技大学电子工程学院,陕西西安710071

出　　处：《西安电子科技大学学报》2024年第5期82-96,共15页Journal of Xidian University

基　　金：国家自然科学基金(62077038,61672405,62176196,62271374)。

摘　　要：针对当前高光谱图像跨域小样本分类领域存在的问题,如低分类精度和有限的泛化能力,提出了一种子空间和存储体的跨域小样本高光谱图像分类方法。该方法改进了一种融合通道注意机制和光谱空间注意机制的特征提取器,以充分提取原始高光谱图像的光谱空间信息。通过对比学习机制,分析小样本之间的多样性和差异性,提升了模型在小样本情况下的判别能力和泛化性能。同时,利用自适应子空间来改进原型网络,以提高嵌入特征的利用率,从而提升了模型的分类精度。最后,引入存储体模块实现跨域对齐,增强了模型在跨域条件下的分类性能。通过迭代训练和不断优化,使用优化后的特征提取器对测试集进行分类。在四个常用的数据集上将文中方法与当前主流的高光谱跨域小样本分类方法进行了比较。实验结果显示,文中方法的分类效果优于其他现有方法,同时还展现出出色的泛化能力和鲁棒性。In response to the challenges in the field of cross-domain few-shot classification of hyperspectral images,such as low classification accuracy and limited generalization capability,this study proposes a novel hyperspectral image classification method based on the subspace and memory bank of cross-domain few-shot learning(SMB-CFSL).A feature extractor is improved that integrates the channel attention mechanism and the spectral-spatial attention mechanism to fully extract the spectral spatial information on original hyperspectral images.By employing the contrastive learning mechanism to analyze the diversity and differences among small samples,the discriminative power and generalization performance of the model are enhanced under the few-shot scenario.Additionally,the prototype network is improved by utilizing adaptive subspace to enhance the utilization of embedding features,leading to improved accuracy in image classification.Finally,a memory bank module is introduced to achieve cross-domain alignment and enhance the classification performance of the model under cross-domain conditions.Through iterative training and continuous optimization,the optimized feature extractor is employed for classification on the testing set.We compare our proposed method with state-of-the-art approaches for cross-domain few-shot classification of hyperspectral images using four widely adopted datasets.Experimental results demonstrate that our method outperforms several existing methods in classification while also exhibiting excellent generalization capability and robustness.

关 键 词：图像分类 跨域小样本 特征提取 子空间 存储体 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置]