Hyperspectral Remote Sensing Data Analysis and Future Challenges

TLDR

Hyperspectral remote sensing has advanced over the past two decades, with airborne and spaceborne sensors providing high spectral, spatial, and temporal resolution that enable fine material identification and physical parameter estimation, yet the high dimensionality, spectral mixing, and measurement degradation pose significant analytical challenges. This paper presents a tutorial and overview of hyperspectral data analysis methods across six key topics—data fusion, unmixing, classification, target detection, physical parameter retrieval, and fast computing. The authors describe state‑of‑the‑art techniques, illustrate them with examples, and point to future challenges and research directions.

Abstract

Hyperspectral remote sensing technology has advanced significantly in the past two decades. Current sensors onboard airborne and spaceborne platforms cover large areas of the Earth surface with unprecedented spectral, spatial, and temporal resolutions. These characteristics enable a myriad of applications requiring fine identification of materials or estimation of physical parameters. Very often, these applications rely on sophisticated and complex data analysis methods. The sources of difficulties are, namely, the high dimensionality and size of the hyperspectral data, the spectral mixing (linear and nonlinear), and the degradation mechanisms associated to the measurement process such as noise and atmospheric effects. This paper presents a tutorial/overview cross section of some relevant hyperspectral data analysis methods and algorithms, organized in six main topics: data fusion, unmixing, classification, target detection, physical parameter retrieval, and fast computing. In all topics, we describe the state-of-the-art, provide illustrative examples, and point to future challenges and research directions.