Hyperspectral remote sensing data analysis

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, blur, and atmospheric effects. In this talk, I will present an overview cross section of some relevant hyperspectral data analysis methods and algorithms, namely, data fusion, unmixing, classification, target detection, physical parameter retrieval, and fast computing. For each topic, I will summarize the mathematical problem involved, give relevant pointers to state-of-the-art algorithm to address these problems, and illustrate experimentally the potentialities and limitations of these algorithms.