Classification of Hyperspectral Images via Multitask Generative Adversarial Networks

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.

Automated summary

In this article, a multitask generative adversarial network (MTGAN) is proposed to address the issue of deep learning models heavily depending on the quantity of available training samples in hyperspectral image classification. By utilizing rich information from unlabeled samples and employing an adversarial learning method, the MTGAN model is able to indirectly improve the discrimination and generalization ability of the classification task. Additionally, skip-layer connections are used to fully explore useful information from shallow layers, resulting in higher performance compared to other state-of-the-art deep learning models.