SNR estimation and systematic disturbance rejection in hyperspectral remotely sensed images of the earth

A great effort is actually devoted to dispose of sensors with increased spectral, spatial, and radiometric resolution, improvements that are ended to obtain quantitative and accurate information of the observed scenes. Topics dealing with the different aspects of sensor calibration are, in this framework, increasingly important. We investigated some relevant problems connected with sensor calibration: the flat-field correction, and the SNR estimate. In a past work we have shown a new algorithm devoted to off-line flat-field correction, that has been shown to correctly work on images gathered by matrix-detectors. In the same work we had also shown a novel approach to SNR evaluation. In this paper we discuss how our model for flat-field correction behaves when applied to data acquired by scanning devices. For images gathered by these sensors we developed a model which correctly predicts the appearance of a spatially-coherent pattern of disturbances, with a characteristic cross-track shape. We also show that our flat-field procedure is able to properly correct the images observed by scanning detectors. Theory and data-reduction algorithms were tested with images acquired by multispectral and hyperspectral imaging systems.