  Deep neural networks have proven to be very effective for computer vision
tasks, such as image classification, object detection, and semantic
segmentation -- these are primarily applied to color imagery and video. In
recent years, there has been an emergence of deep learning algorithms being
applied to hyperspectral and multispectral imagery for remote sensing and
biomedicine tasks. These multi-channel images come with their own unique set of
challenges that must be addressed for effective image analysis. Challenges
include limited ground truth (annotation is expensive and extensive labeling is
often not feasible), and high dimensional nature of the data (each pixel is
represented by hundreds of spectral bands), despite being presented by a large
amount of unlabeled data and the potential to leverage multiple sensors/sources
that observe the same scene. In this chapter, we will review recent advances in
the community that leverage deep learning for robust hyperspectral image
analysis despite these unique challenges -- specifically, we will review
unsupervised, semi-supervised and active learning approaches to image analysis,
as well as transfer learning approaches for multi-source (e.g. multi-sensor, or
multi-temporal) image analysis.
