In the recent years, hyperspectral imaging (HSI) has gained considerably popularity among computer vision researchers for its potential in solving remote sensing problems, especially in agriculture field. However, HSI classification is a complex task due to the high redundancy of spectral bands, limited training samples, and non-linear relationship between spatial position and spectral bands. Fortunately, deep learning techniques have shown promising results in HSI analysis. This literature review explores recent applications of deep learning approaches such as Autoencoders, Convolutional Neural Networks (1D, 2D, and 3D), Recurrent Neural Networks, Deep Belief Networks, and Generative Adversarial Networks in agriculture. The performance of these approaches has been evaluated and discussed on well-known land cover datasets including Indian Pines, Salinas Valley, and Pavia University.

翻译：近年来，高光谱成像（HSI）因其在解决遥感问题（尤其是农业领域）方面的潜力，在计算机视觉研究者中获得了显著关注。然而，由于光谱波段的高度冗余、有限的训练样本以及空间位置与光谱波段之间的非线性关系，HSI分类是一项复杂任务。幸运的是，深度学习技术在HSI分析中已展现出令人鼓舞的成果。本文献综述探讨了深度学习方法（如自编码器、卷积神经网络（一维、二维和三维）、循环神经网络、深度信念网络和生成对抗网络）在农业领域的最新应用。这些方法的性能已在包括Indian Pines、Salinas Valley和Pavia University在内的知名土地覆盖数据集上进行了评估与讨论。