  Hyperspectral Imaging (HSI) has been extensively utilized in many real-life
applications because it benefits from the detailed spectral information
contained in each pixel. Notably, the complex characteristics i.e., the
nonlinear relation among the captured spectral information and the
corresponding object of HSI data make accurate classification challenging for
traditional methods. In the last few years, deep learning (DL) has been
substantiated as a powerful feature extractor that effectively addresses the
nonlinear problems that appeared in a number of computer vision tasks. This
prompts the deployment of DL for HSI classification (HSIC) which revealed good
performance. This survey enlists a systematic overview of DL for HSIC and
compared state-of-the-art strategies of the said topic. Primarily, we will
encapsulate the main challenges of traditional machine learning for HSIC and
then we will acquaint the superiority of DL to address these problems. This
survey breakdown the state-of-the-art DL frameworks into spectral-features,
spatial-features, and together spatial-spectral features to systematically
analyze the achievements (future directions as well) of these frameworks for
HSIC. Moreover, we will consider the fact that DL requires a large number of
labeled training examples whereas acquiring such a number for HSIC is
challenging in terms of time and cost. Therefore, this survey discusses some
strategies to improve the generalization performance of DL strategies which can
provide some future guidelines.
