Radiometric normalization with multi-image pseudo-invariant features

Luigi Barazzetti; Marco Gianinetto; Marco Scaioni

doi:10.1117/12.2240705

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

Luigi Barazzetti, Marco Gianinetto, Marco Scaioni

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.

Citation Download Citation

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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