Over the years, advances in sensor technologies have enhanced spatial, temporal, spectral, and radiometric resolutions, thus significantly improving the size, resolution, and quality of imagery. These vast developments have inspired improvement in various hyperspectral images (HSI) classification applications such as land cover mapping, vegetation classification, urban monitoring, and understanding which are essential for better utilization of Earth’s resources. HSI classification requires superior algorithms with greater accuracy, less computational complexity, and robustness to extract rich, spectral-spatial information. Deep convolution neural networks (DCCNs) have revolutionized image classification experience, with robust architectures being proposed from time to time. However, insufficient training samples have been earmarked as a significant bottleneck for supervised HSI classification and have not been fully explored in literature. To stimulate further research, this paper reviews current methods that handle labeled data insufficiency and the current feature learning methods for HSI classification using DCNNs. It also presents various methods’ results on the three most popular public HSI datasets, together with intuitive observations motivating future research by the hyperspectral community.

多年来，传感器技术的进步提高了空间、时间、光谱和辐射分辨率，从而显着提高了图像的尺寸、分辨率和质量。这些巨大的发展激发了各种高光谱图像 (HSI) 分类应用的改进，例如土地覆盖制图、植被分类、城市监测和理解，这些对于更好地利用地球资源至关重要。HSI 分类需要具有更高准确性、更低计算复杂性和鲁棒性的高级算法，以提取丰富的光谱空间信息。深度卷积神经网络 (DCCN) 已经彻底改变了图像分类体验，不时提出稳健的架构。然而，训练样本不足已被指定为监督 HSI 分类的一个重要瓶颈，并且尚未在文献中得到充分探讨。为了激发进一步的研究，本文回顾了当前处理标记数据不足的方法以及当前使用 DCNN 进行 HSI 分类的特征学习方法。它还介绍了三种最流行的公共 HSI 数据集的各种方法结果，以及激发高光谱社区未来研究的直观观察。