Advances in Deep Learning for Hyperspectral Image Analysis--Addressing Challenges Arising in Practical Imaging Scenarios

TL;DR

This paper reviews recent deep learning advances for hyperspectral image analysis, focusing on overcoming challenges like limited labels and high data dimensionality through unsupervised, semi-supervised, active, and transfer learning methods.

Contribution

It provides a comprehensive overview of novel deep learning techniques tailored for hyperspectral imaging challenges in remote sensing and biomedicine.

Findings

Unsupervised and semi-supervised methods improve analysis with limited labels.

Transfer learning enables multi-source hyperspectral data integration.

Active learning reduces labeling effort effectively.

Abstract

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.