Quality criteria benchmark for hyperspectral imagery

Hyperspectral data appear to be of a growing interest over the past few years. However, applications for hyperspectral data are still in their infancy as handling the significant size of the data presents a challenge for the user community. Efficient compression techniques are required, and lossy compression, specifically, will have a role to play, provided its impact on remote sensing applications remains insignificant. To assess the data quality, suitable distortion measures relevant to end-user applications are required. Quality criteria are also of a major interest for the conception and development of new sensors to define their requirements and specifications. This paper proposes a method to evaluate quality criteria in the context of hyperspectral images. The purpose is to provide quality criteria relevant to the impact of degradations on several classification applications. Different quality criteria are considered. Some are traditionnally used in image and video coding and are adapted here to hyperspectral images. Others are specific to hyperspectral data.We also propose the adaptation of two advanced criteria in the presence of different simulated degradations on AVIRIS hyperspectral images. Finally, five criteria are selected to give an accurate representation of the nature and the level of the degradation affecting hyperspectral data

Quality metrics for spectral estimation

Tesina realitzada en col.laboració amb Centre de Desenvolupament de Sensors, Instrumentació i SistemesThe quantitative assessment of the spectral estimation quality in multispectral imaging systems is an active field of research. The design and optimization of multispectral imaging systems are very dependent on how the cost function is selected. Several spectral estimation metrics have been used depending on the attribute it is intended to measure: visual matching, correlation of spectral curves or reduction of metamerism. The purpose of this project is to analyze various metrics that have been used for spectral matches and to show the appropriateness and weakness of each metric