Case Study of a Calibration Problem in Acquired Hyperspectral Images.

Hyperspectral imaging allows the classification and localization of materials for diverse applications. The existing datasets are either limited to a single image or created for specific applications. In our work, we need a dataset of urban materials for classification. However, the illumination and acquisition conditions are varying over time. This impacts the raw hyperspectral images and drives the need of making images independent from the acquisition conditions. To deal with it, classical approaches are based on the conversion of raw images to radiance and reflectance. Though many studies have been conducted on reflectance images and on ways to improve their robustness to such changes, the selection of a proper radiance image has yet to be studied. In this paper, we first describe the creation of a dataset, then study two methods for correcting the radiance computation and comment the results of the proposed process.