Multiscale statistical image destriping algorithm

Vincent Martin; Arnaud Kelbert

doi:10.1117/12.2195002

15 October 2015 Multiscale statistical image destriping algorithm

Vincent Martin, Arnaud Kelbert

Proceedings Volume 9643, Image and Signal Processing for Remote Sensing XXI; 96430B (2015) https://doi.org/10.1117/12.2195002
Event: SPIE Remote Sensing, 2015, Toulouse, France

Abstract

This paper presents a multi-scale framework for image destriping algorithms, allowing estimating image normalization coefficients adapted to stripe artifacts covering a large range of spatial frequencies. This algorithm can address destriping of push- and whisk-broom satellite images, which often present residual striping patterns along the scanning direction. The proposed method is however generic and can be applied to any image including unidirectional structured noise, e.g. vertically or horizontally. Only a single spectral image channel is required, whereas extension to multi-channel imagery is straightforward. It is an unsupervised method, which is essential to process any acquisition in an operational ground segment. This paper combines the proposed framework with a MAP-estimation-based state-of-the art destriping algorithm and presents applications to real satellite imagery.

Citation Download Citation

Vincent Martin and Arnaud Kelbert "Multiscale statistical image destriping algorithm", Proc. SPIE 9643, Image and Signal Processing for Remote Sensing XXI, 96430B (15 October 2015); https://doi.org/10.1117/12.2195002

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KEYWORDS

Sensors

Image processing

Image sensors

Satellite imaging

Detection and tracking algorithms

Satellites

Earth observing sensors

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