深度学习在高光谱图像分类领域的研究现状与展望

Hyperspectral imagery (HSI) classification occupies an important place in the earth observation technology of hyperspectral remote sensing, and it is widely used in both military and civil fields. However, due to HSI's characteristics including high dimensionality in data, high correlation between spectrum and mixing in spectrum, HSI classification faces great challenges. In recent years, as new deep learning technology emerges, the HSI classification methods based on deep learning have achieved some breakthroughs in methodology and performance and provided new opportunities for the research of HSI classification. In this paper, we review the research background, actuality of HSI classification technologies and several common datasets. Then, we provide a brief overview of several typical deep learning models. Finally, we introduce some deep learning based HSI classification methods in detail, summarize the main function and existing problems of deep learning in HSI classification, and present some prospects for future work.