Deep Learning for Hyperspectral Image Analysis, Part II: Applications to Remote and Biomedicine

Deep neural networks are emerging as a popular choice for hyperspectral image analysis—compared with other machine learning approaches, they are more effective for a variety of applications in hyperspectral imaging. Part I (Chap. 3) introduces the fundamentals of deep learning algorithms and techniques deployed with hyperspectral images. In this chapter (Part II), we focus on application-specific nuances and design choices with respect to deploying such networks for robust analysis of hyperspectral images. We provide quantitative and qualitative results with a variety of deep learning architectures, and compare their performance to baseline state-of-the-art methods for both remote sensing and biomedical image analysis tasks. In addition to surveying recent developments in these areas, our goal in these two chapters is to provide guidance on how to utilize such algorithms for multichannel optical imagery. With that goal, we also provide code and example datasets used in this chapter.