Recent advances in neural networks have made great progress in the hyperspectral image (HSI) classification. However, the overfitting effect, which is mainly caused by complicated model structure and small training set, remains a major concern. Reducing the complexity of the neural networks could prevent overfitting to some extent, but also declines the networks’ ability to express more abstract features. Enlarging the training set is also difficult, for the high expense of acquisition and manual labeling. In this paper, we propose an abundance-based multi-HSI classification method. Firstly, we convert every HSI from the spectral domain to the abundance domain by a dataset-specific autoencoder. Secondly, the abundance representations from multiple HSIs are collected to form an enlarged dataset. Lastly, we train an abundance-based classifier and employ the classifier to predict over all the involved HSI datasets. Different from the spectra that are usually highly mixed, the abundance features are more representative in reduced dimension with less noise. This benefits the proposed method to employ simple classifiers and enlarged training data, and to expect less overfitting issues. The effectiveness of the proposed method is verified by the ablation study and the comparative experiments.

神经网络的最新进展在高光谱图像（HSI）分类中取得了长足的进步。但是，主要由复杂的模型结构和小的训练集引起的过拟合效果仍然是主要关注的问题。降低神经网络的复杂性可以在某种程度上防止过拟合，但也会降低神经网络表达更多抽象特征的能力。由于购置和手工贴标签的高额费用，扩大培训范围也是困难的。在本文中，我们提出了一种基于丰度的多HSI分类方法。首先，我们通过特定于数据集的自动编码器将每个HSI从频谱域转换为丰度域。其次，收集来自多个HSI的丰度表示，以形成扩大的数据集。最后，我们训练基于丰度的分类器，并使用分类器来预测所有涉及的HSI数据集。与通常高度混合的光谱不同，丰度特征在尺寸减小且噪声较小的情况下更具代表性。这有益于所提出的方法，该方法采用简单的分类器和扩大的训练数据，并期望减少过度拟合的问题。通过消融研究和对比实验验证了该方法的有效性。