ABSTRACT

Recently, deep learning models have achieved remarkable results in hyperspectral imagery (HSI) classification. However, most deep learning models tend to struggle due to the serious overfitting problem under the condition of small sample. For this purpose, we design an end-to-end classification framework and optimize it with the idea of meta-learning, to improve the accuracy and robustness of HSI small sample classification. Specifically, the embedding network based on 3D convolution is used to extract spatial-spectral features in HSI, the temporal convolution is adopted to analyse the abstract relationship between feature vectors by aggregating contextual information, and the attention mechanism is introduced to pinpoint the critical information conducive to classification, so as to further improve the classification accuracy. We adopt the typical mechanism of ‘meta-training + fine-tuning’ to optimize the designed model, enabling the model to acquire stronger generalization ability through a large number of different tasks. Experiments based on three public HSI data sets demonstrate that our method outperforms the regular deep learning models under the condition of small sample.

摘要

最近，深度学习模型在高光谱图像（HSI）分类中取得了显著成果。但是，大多数深度学习模型由于在小样本情况下的严重过度拟合问题而趋于挣扎。为此，我们设计了一个端到端的分类框架，并通过元学习的思想对其进行了优化，以提高HSI小样本分类的准确性和鲁棒性。具体而言，基于3D卷积的嵌入网络用于提取HSI中的空间光谱特征，通过时间卷积通过聚集上下文信息来分析特征向量之间的抽象关系，并引入注意机制以找出有助于信息的关键信息。进行分类，以进一步提高分类的准确性。我们采用“元训练+微调”的典型机制来优化设计的模型，从而使模型能够通过大量不同的任务获得更强的泛化能力。基于三个公共HSI数据集的实验表明，在小样本情况下，我们的方法优于常规的深度学习模型。