  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.
