Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art

Abstract

Recent advances in airborne and spaceborne hyperspectral imaging technology have provided end users with rich spectral, spatial, and temporal information. They have made a plethora of applications feasible for the analysis of large areas of the Earth’s surface. However, a significant number of factors - such as the high dimensions and size of the hyperspectral data, the lack of training samples, mixed pixels, light-scattering mechanisms in the acquisition process, and different atmospheric and geometric distortions - make such data inherently nonlinear and complex, which poses major challenges for existing methodologies to effectively process and analyze the data sets. Hence, rigorous and innovative methodologies are required for hyperspectral image (HSI) and signal processing and have become a center of attention for researchers worldwide. This article offers a comprehensive tutorial/overview focusing specifically on hyperspectral data analysis, which is categorized into seven broad topics: classification, spectral unmixing, dimensionality reduction (DR), resolution enhancement, HSI denoising and restoration, change detection (CD), and fast computing. For each topic, we provide a synopsis of the state-of-the-art approaches and numerical results for validating and evaluating different methodologies, followed by a discussion of future challenges and research directions.