Hyperspectral Remote Sensing Data Analysis and Future Challenges

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. I. IntroductIonH yperspectral remote sensing is concerned with the extraction of information from objects or scenes lying on the Earth surface, based on their radiance acquired by airborne or spaceborne sensors [1], [2].Hyperspectral sensing, namely its imaging modality termed hyperspectral imaging, has been increasingly used in applications at lab scale (e.g., food safety, pharmaceutical process monitoring and quality control, biomedical, industrial, biometric, and forensic) using small, commercial, high spatial