Information extraction from remotely sensed images acquired in the visible and near-infrared (VNIR) frequency range strongly depends on an accurate cloud pixel screening. Indeed, many remote sensing applications require a preliminary cloud detection phase to obtain profitable results. In this paper we propose to integrate the potential of the MAP-MRF methodology with the multispectral approach for augmenting the capability of the algorithm to detect cloudy pixels. In particular the proposed technique combines information from some SEVIRI sensor channels (in particular the channels 0.64ìm, 1.6ìm, 3.9ìm, 7.3ìm and 10.8ìm) with the classification obtained by the MAP-MRF method in the 0.8ìm channel in order to discriminate between snowy and cloudy pixels. The validation is performed on challenging images of Alps mountains acquired by the SEVIRI sensor during winter months. Results show significant improvements with respect to existing methods. In particular we highlight a more precise classification at the cloud borders and a considerable reduction of unsolicited holes inside the cloud masse

A multispectral spatio-temporal approach for cloud screening of remotely sensed image

ADDESSO, PAOLO;CONTE, ROBERTO;LONGO, Maurizio;RESTAINO, Rocco;VIVONE, GEMINE
2011-01-01

Abstract

Information extraction from remotely sensed images acquired in the visible and near-infrared (VNIR) frequency range strongly depends on an accurate cloud pixel screening. Indeed, many remote sensing applications require a preliminary cloud detection phase to obtain profitable results. In this paper we propose to integrate the potential of the MAP-MRF methodology with the multispectral approach for augmenting the capability of the algorithm to detect cloudy pixels. In particular the proposed technique combines information from some SEVIRI sensor channels (in particular the channels 0.64ìm, 1.6ìm, 3.9ìm, 7.3ìm and 10.8ìm) with the classification obtained by the MAP-MRF method in the 0.8ìm channel in order to discriminate between snowy and cloudy pixels. The validation is performed on challenging images of Alps mountains acquired by the SEVIRI sensor during winter months. Results show significant improvements with respect to existing methods. In particular we highlight a more precise classification at the cloud borders and a considerable reduction of unsolicited holes inside the cloud masse
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/3113051
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