Mining spectral libraries to study sensors' discrimination ability

In remote sensing data classification, the ability to discriminate different land cover or material types is directly linked with the spectral resolution and sampling provided by the optical sensor. Several previous studies showed that the spectral resolution is a critical issue, especially to discriminate different land covers in urban areas. In spite of the increasing avaibility of hyperspectral data, multispectral optical sensors on board of several satellites are still acquiring everyday a massive amount of data with a relatively poor spectral resolution (i.e. usually about 4 to 7 spectral bands). These remotely sensed data are intensively used for Earth observation regardless of their limited spectral resolution. In this paper, we propose to study the capacity of discrimination of several of these optical sensors : Pleiades, QuickBird, SPOT5, Ikonos, Landsat, etc. To achieve this goal, we used different spectral libraries which provide spectra of materials and land covers generally with a fine spectral resolution (from 350 to 2400nm with 10nm bandwidth). These spectra were extracted from these libraries and convolved with the Relative Spectral Responses (RSR) of each sensor to create spectra at the sensors' resolutions. Then, these reduced spectra were evaluated thanks to classical separability indices and machine learning tools. This study focuses on the capacity of each sensor to discriminate different materials according to its spectral resolution.