## 1 Introduction  
Hyperspectral images (HSI) consist of continuous, narrow spectral bands, reflecting not only the spatial information but also the spectral information of remotely sensed objects. With high-resolution spectral information and large-scale spatial information, hyperspectral images have demonstrated outstanding capabilities in many fields, such as geological exploration and medicine [1]. Hyperspectral image classification is an important application. Many machine learning algorithms have been designed and applied to HSI classification. However, the small sample problem still makes HSI classification very challenging. In the past few years, many deep-learning-based HSI classification methods have achieved impressive results. The authors of [2] proposed a spectral attention network that purposefully suppresses less useful spectral bands and enhances valuable spectral bands to solve the HSI classification problem. In the work of [PERSON] et al. [3], a deep fully convolutional network (FCN) was utilized to take the entire data cube of an HSI as an input, which can then effectively integrate long-range contextual information. The authors of [4] designed a convolutional neural network (CNN)-based network that can merge features at multiple scales. However, CNNs can only handle relationships in spatial neighborhoods. The authors of [5] proposed a deep feature aggregation framework driven by a graph convolutional network (DFAGCN) model. The model first extracts features using CNN and then introduces a GCN-based model [6] and, finally, fuses the features using a weighted concatenation method. The authors of [7] proposed small-batch GCN (miniGCN), which trains the network in small batches, extracts different features using CNN and miniGCN, and fuses the features using three fusion strategies for HSI classification. However, most methods based on deep learning theory require a sufficient number of labeled samples for training, otherwise it is difficult to achieve the desired classification results. Unfortunately, off-the-shelf labeled hyperspectral images are rare, and labeling new images can be both time-consuming and expensive [8]. A promising alternative direction lies in cross-domain generation of hyperspectral data. To improve the classification performance of a dataset with a limited number of labeled samples (called the target scene), we can use information from another similar dataset with sufficient labeled samples (called the source scene) [9]. However, due to different imaging sensors, the source and target scenes may contain different feature distributions, which leads to a non-negligible classification problem. There is also a large amount of distortion and noise on the generated hyperspectral data. In recent years, models based on generative adversarial networks (GANs) [10] have been used for migration learning in remote sensing. GANs can generate fake samples that are indistinguishable from the real ones. Typically, a GAN-based model consists of a generator and a discriminator. It is relatively feasible to transfer images from one scene to another by adversarial learning [11]. The auxiliary classifier GAN (AC-GAN) [12] introduces labeling information into the training process of GAN. Another approach, CycleGAN (the cycle-consistent generative adversarial network), was proposed by [PERSON] et al. [13] and is a modified model of GAN that creates a bidirectional mapping between two different scenes. Many GAN-based methods achieve alignment of cross-scene feature distributions by minimizing domain differences [14]. In this paper, we propose and design a novel adversarial neural network architecture for cross-domain data generation, termed Hyper-CycleGAN, which is targeted toward hyperspectral data. Specifically, the Hyper-CycleGAN architecture makes the following contributions:  
* The architecture initiates with a bidirectional mapping network to ensure consistency in data distribution between source and target scenes.