Improving radiometry of imaging spectrometers by using programmable spectral regions of interest

Programmable imaging spectrometers can be adjusted to fit specific application requirements that differ from the instrument initial spectral design goals. Sensor spectral characteristics and its signal-to-noise ratio (SNR) can be changed by applying customized online binning patterns. We present a software utility that generates application driven spectral binning patterns by using an SNR dependent sensor model. The utility, named BinGO (BInning patterN Generator and Optimiser), is used to produce predefined binning patterns that either (a) allow an existing imaging spectrometer to optimize its spectral characteristics for a specific application, (b) allow an existing imaging spectrometer to spectral and/or spatially emulate another instrument, or (c) design new multispectral or imaging spectrometer missions (i.e. spaceborne, airborne, terrestrial). We present a variety of BinGO case studies, including the simulation of airborne (APEX) [Itten, K.I. et al., 2008. APEX - The hyperspectral ESA Airborne Prism Experiment. Sensors 8(1), 1-25], spaceborne (SENTINEL 111) [Nieke, J., Frerick, J., Stroede, J., Mavrocordatos, C., Berruti, B., 2008. Status of the optical payload and processor development of ESA's Sentinel 3 mission. In: Proceedings of the Geoscience and Remote Sensing Symposium IGARSS 2008, pp. 427-430], as well as scientific and performance optimized approaches. We conclude that the presented approach can successfully be used to increase the efficiency of spectral information retrieval by using imaging spectroscopy data and to simulate various missions and requirements, finally supporting proper trade-off decisions to be made between performance optimization and scientific requirements. In addition, if specific sensor parameters are known, BinGO can also model other imaging spectrometers. (C) 2009 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.