  With the rapid development of deep learning technology and improvement in
computing capability, deep learning has been widely used in the field of
hyperspectral image (HSI) classification. In general, deep learning models
often contain many trainable parameters and require a massive number of labeled
samples to achieve optimal performance. However, in regard to HSI
classification, a large number of labeled samples is generally difficult to
acquire due to the difficulty and time-consuming nature of manual labeling.
Therefore, many research works focus on building a deep learning model for HSI
classification with few labeled samples. In this article, we concentrate on
this topic and provide a systematic review of the relevant literature.
Specifically, the contributions of this paper are twofold. First, the research
progress of related methods is categorized according to the learning paradigm,
including transfer learning, active learning and few-shot learning. Second, a
number of experiments with various state-of-the-art approaches has been carried
out, and the results are summarized to reveal the potential research
directions. More importantly, it is notable that although there is a vast gap
between deep learning models (that usually need sufficient labeled samples) and
the HSI scenario with few labeled samples, the issues of small-sample sets can
be well characterized by fusion of deep learning methods and related
techniques, such as transfer learning and a lightweight model. For
reproducibility, the source codes of the methods assessed in the paper can be
found at https://github.com/ShuGuoJ/HSI-Classification.git.
