An overview of hyperspectral image feature extraction, classification methods and the methods based on small samples

Hyperspectral image (HSI) contains rich spatial and spectral information, which has been widely used in resource exploration, ecological environment monitoring, land cover classification and target recognition. However, the nonlinearity of HSI data and the strong correlation between bands bring difficulties to HSI application, especially the limited available training samples will restrict the improvement of classification accuracy. This paper systematically reviews the research progress of feature extraction methods and classification algorithms for HSI classification in recent years. In addition, five types of small sample strategies are elaborated, which solve the small sample problem in HSI classification from different perspectives. The study points out that small sample strategy will be the focus of HSI classification research in the future, and solving the problem of small sample classification can greatly promote the application of HSI technology.

Applied Spectroscopy Reviews