Recently, the deep learning models have achieved great success in hyperspectral images (HSI) classification. However, most of the deep learning models fail to obtain satisfactory results under the condition of small samples due to the contradiction between the large parameter space of the deep learning models and the insufficient labeled samples in HSI. To address the problem, a deep model based on the induction network is designed in this article to improve the classification performance of HSI under the condition of small samples. Specifically, the typical meta-training strategy is adopted, enabling the model to acquire stronger generalization ability, so as to accurately distinguish the new classes with only a few labeled samples (e.g., five samples per class). Moreover, in order to deal with the disturbance caused by the various characteristics of the samples in the same class in HSI, the class-wise induction module is introduced utilizing the dynamic routing algorithm, which can induce the sample-wise representations to the class-wise level representations. The obtained class-wise level representations possess better separability, allowing the designed model to generate more accurate and robust classification results. Extensive experiments are carried out on three public HSI to verify the effectiveness of the proposed method. The results demonstrate that our method outperforms existing deep learning methods under the condition of small samples.

中文翻译：

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用于高光谱图像小样本分类的深度归纳网络

最近，深度学习模型在高光谱图像（HSI）分类方面取得了巨大成功。然而，由于深度学习模型的大参数空间和HSI中标记样本不足之间的矛盾，大多数深度学习模型在小样本条件下未能获得令人满意的结果。针对该问题，本文设计了一种基于归纳网络的深度模型，以提高小样本条件下HSI的分类性能。具体来说，采用典型的元训练策略，使模型获得更强的泛化能力，从而仅用少量标记样本（例如每类五个样本）就能准确区分新类。而且，为了处理HSI中同一类样本的各种特征引起的干扰，利用动态路由算法引入了class-wise Induction模块，可以将sample-wise表示归纳到class-wise级别表示。获得的类级别表示具有更好的可分离性，使设计的模型能够生成更准确和鲁棒的分类结果。在三个公共 HSI 上进行了广泛的实验，以验证所提出方法的有效性。结果表明，我们的方法在小样本条件下优于现有的深度学习方法。这可以将样本表示引入到类级别表示。获得的类级别表示具有更好的可分离性，使设计的模型能够生成更准确和鲁棒的分类结果。在三个公共 HSI 上进行了广泛的实验，以验证所提出方法的有效性。结果表明，我们的方法在小样本条件下优于现有的深度学习方法。这可以将样本表示引入到类级别表示。获得的类级别表示具有更好的可分离性，使设计的模型能够生成更准确和鲁棒的分类结果。在三个公共 HSI 上进行了广泛的实验，以验证所提出方法的有效性。结果表明，我们的方法在小样本条件下优于现有的深度学习方法。