面向小样本高光谱影像分类的轻量化关系网络

Lightweight Relational Network for Small Sample Hyperspectral Image Classification

近年来,基于深度学习的高光谱影像分类取得重要进展,针对高光谱影像分类训练样本稀缺的情况,提出一种结合注意力机制的轻量化关系网络(lightweight attention depth-wise relation network, LWAD-RN),以解决高光谱影像小样本分类问题。该网络由嵌入层和关联层组成,在嵌入层采用结合注意力机制的轻量化卷积神经网络提取像元特征,同时引入稠密网络结构;在关联层计算关联值进行分类,并采用基于任务的模式训练网络。利用3组公开的高光谱影像数据进行对比实验,结果表明,LWAD-RN能够有效提升小样本条件下(每类5个训练样本)的分类精度,同时提高了模型训练和分类效率。

Objectives: In recent years, hyperspectral images classification based on deep learning has made important progress. In view of the scarcity of training samples for hyperspectral image classification, this paper proposes a lightweight attention depth-wise relation network( LWAD-RN) to solve the problem of small sample hyperspectral image classification.Methods: The network consists of an embedding layer and a relation layer. In the embedding layer, a lightweight convolutional neural network combining attention mechanism is used to extract pixel features, and a dense network structure is introduced. The relation value is calculated in the relation layer for classification, and the task-based mode is used to train the network.Three groups of public hyperspectral image datasets are used to implement experiments.Results and Conclusions: The results show that LWAD-RN can effectively improve the classification accuracy under the condition of small samples(5 training samples per category), and the efficiency of model training and classification is improved.The proposed LWAD-RN can obtain ideal classification accuracy under the condition of small samples, and the lightweight network structure can improve the model training and classification efficiency. However, under the condition of small samples, the quality of training samples will have an important impact on the performance of the model. Therefore, follow-up studies should be conducted on how to select high-quality training samples more accurately and efficiently to ensure the stability of the model and better meet the needs of practical application.