  Hyperspectral image (HSI) classification has become a hot topic in the field
of remote sensing. In general, the complex characteristics of hyperspectral
data make the accurate classification of such data challenging for traditional
machine learning methods. In addition, hyperspectral imaging often deals with
an inherently nonlinear relation between the captured spectral information and
the corresponding materials. In recent years, deep learning has been recognized
as a powerful feature-extraction tool to effectively address nonlinear problems
and widely used in a number of image processing tasks. Motivated by those
successful applications, deep learning has also been introduced to classify
HSIs and demonstrated good performance. This survey paper presents a systematic
review of deep learning-based HSI classification literatures and compares
several strategies for this topic. Specifically, we first summarize the main
challenges of HSI classification which cannot be effectively overcome by
traditional machine learning methods, and also introduce the advantages of deep
learning to handle these problems. Then, we build a framework which divides the
corresponding works into spectral-feature networks, spatial-feature networks,
and spectral-spatial-feature networks to systematically review the recent
achievements in deep learning-based HSI classification. In addition,
considering the fact that available training samples in the remote sensing
field are usually very limited and training deep networks require a large
number of samples, we include some strategies to improve classification
performance, which can provide some guidelines for future studies on this
topic. Finally, several representative deep learning-based classification
methods are conducted on real HSIs in our experiments.
