Radiometric normalization with multi-image pseudo-invariant features

Luigi Barazzetti; Marco Gianinetto; Marco Scaioni

doi:10.1117/12.2240705

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12 August 2016 Radiometric normalization with multi-image pseudo-invariant features

Luigi Barazzetti, Marco Gianinetto, Marco Scaioni

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Proceedings Volume 9688, Fourth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2016); 968807 (2016) https://doi.org/10.1117/12.2240705
Event: Fourth International Conference on Remote Sensing and Geoinformation of the Environment, 2016, Paphos, Cyprus

Abstract

Radiometric image normalization is one of the basic pre-processing methods used in satellite time series analysis. This paper presents a new multi-image approach able to estimate the parameters of relative radiometric normalization through a multiple and simultaneous regression with a dataset of a generic number of images. The method was developed to overcome the typical drawbacks of standard one-to-one techniques, where image pairs are independently processed. The proposed solution is based on multi-image pseudo-invariant features incorporated into a unique regression solved via Least Squares. Results for both simulated and real data are presented and discussed.

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Luigi Barazzetti, Marco Gianinetto, and Marco Scaioni "Radiometric normalization with multi-image pseudo-invariant features", Proc. SPIE 9688, Fourth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2016), 968807 (12 August 2016); https://doi.org/10.1117/12.2240705

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