Optical remote sensing images are usually acquired according to the classical pushbroom principle. A linear array of CCD detectors, placed in the focal plane of the telescope, acquires a scanline over an integration time. The satellite's motion along its orbit, which is perpendicular to the linear array, ensures acquisition of successive lines. Because of inter-detector sensitivity differences, the image of a uniform landscape is striped vertically. Detector normalization aims at correcting these relative sensitivities and delivering uniform images of uniform areas. Determination of inter-detector coefficients requires observation of one uniform landscape, provided that each detector behaves linearly.
High resolution optical satellites like the future French PLEIADES-HR have to face a lack of signal, which moves the useful signal range towards the non-linear part of the detector response. For such designs, normalization has to be run with a non-linear model : this is a cost-effective way to improve image quality at low radiances and relax detector sorting. Regarding in-flight operations, non-linear parameters identification requires observation of several uniform landscapes and may be actually very difficult to run, because of the uniformity constraint.
An efficient way to bypass the quest of uniformity is to use the satellite agility in order to align the ground projection of the scanline on the ground velocity. This weird viewing principle allows all the detectors to view the same landscape. Thus, non-linear normalization coefficients can be computed by a histogram matching method.
The goal of this paper is to present the Pleiades-HR non-linear normalization model, the suited steered mode and the method to compute the coefficients within Pleiades-HR context.