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.

Radiometric normalization with multi-image pseudo-invariant features

BARAZZETTI, LUIGI;GIANINETTO, MARCO;SCAIONI, MARCO
2016-01-01

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.
2016
Proceedings of SPIE - The International Society for Optical Engineering
9781628419238
Atmospheric correction; Multi-image pseudo-invariant feature; Radiometric normalization; Satellite time series analysis; Electronic, Optical and Magnetic Materials; Condensed Matter Physics; Computer Science Applications1707 Computer Vision and Pattern Recognition; Applied Mathematics; Electrical and Electronic Engineering
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/999358
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