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Proceedings Paper

Radiometric normalization with multi-image pseudo-invariant features
Author(s): Luigi Barazzetti; Marco Gianinetto; Marco Scaioni
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Paper 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.

Paper Details

Date Published: 12 August 2016
PDF: 9 pages
Proc. SPIE 9688, Fourth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2016), 968807 (12 August 2016); doi: 10.1117/12.2240705
Show Author Affiliations
Luigi Barazzetti, Politecnico di Milano (Italy)
Marco Gianinetto, Politecnico di Milano (Italy)
Marco Scaioni, Politecnico di Milano (Italy)
Tongi Univ. (China)


Published in SPIE Proceedings Vol. 9688:
Fourth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2016)
Kyriacos Themistocleous; Diofantos G. Hadjimitsis; Silas Michaelides; Giorgos Papadavid, Editor(s)

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