Deep learning has shown its huge potential in the field of hyperspectral image (HSI) classification. However, most of the deep learning models heavily depend on the quantity of available training samples. In this article, we propose a multitask generative adversarial network (MTGAN) to alleviate this issue by taking advantage of the rich information from unlabeled samples. Specifically, we design a generator network to simultaneously undertake two tasks: the reconstruction task and the classification task. The former task aims at reconstructing an input hyperspectral cube, including the labeled and unlabeled ones, whereas the latter task attempts to recognize the category of the cube. Meanwhile, we construct a discriminator network to discriminate the input sample coming from the real distribution or the reconstructed one. Through an adversarial learning method, the generator network will produce real-like cubes, thus indirectly improving the discrimination and generalization ability of the classification task. More importantly, in order to fully explore the useful information from shallow layers, we adopt skip-layer connections in both reconstruction and classification tasks. The proposed MTGAN model is implemented on three standard HSIs, and the experimental results show that it is able to achieve higher performance than other state-of-the-art deep learning models.

中文翻译：

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通过多任务生成对抗网络对高光谱图像进行分类

深度学习在高光谱图像（HSI）分类领域显示出巨大的潜力。然而，大多数深度学习模型严重依赖于可用训练样本的数量。在本文中，我们提出了一个多任务生成对抗网络 (MTGAN)，通过利用来自未标记样本的丰富信息来缓解这个问题。具体来说，我们设计了一个生成器网络来同时承担两个任务：重建任务和分类任务。前一个任务旨在重建输入的高光谱立方体，包括标记和未标记的高光谱立方体，而后一个任务试图识别立方体的类别。同时，我们构建了一个鉴别器网络来区分来自真实分布或重构分布的输入样本。通过对抗性学习方法，生成器网络会产生类似真实的立方体，从而间接提高分类任务的判别和泛化能力。更重要的是，为了充分挖掘来自浅层的有用信息，我们在重建和分类任务中都采用了跳跃层连接。提出的 MTGAN 模型在三个标准 HSI 上实现，实验结果表明它能够实现比其他最先进的深度学习模型更高的性能。我们在重建和分类任务中都采用了跳跃层连接。提出的 MTGAN 模型在三个标准 HSI 上实现，实验结果表明它能够实现比其他最先进的深度学习模型更高的性能。我们在重建和分类任务中都采用了跳跃层连接。提出的 MTGAN 模型在三个标准 HSI 上实现，实验结果表明它能够实现比其他最先进的深度学习模型更高的性能。