This paper introduces a novel method for estimation of snow/no-snow labels for cloud-obscured pixels in order to enable an accurate mapping of the snow-covered area (SCA) in time series. The proposed method leverages the embedded information in multitemporal correlation between the presence/absence of snow and environmental factors including the topographical elevation, date of acquisition, and the cloud obscuration duration. The proposed method is built upon three main steps: i) classification of single date images into three classes (snow, no-snow, and cloud), ii) estimation of conditional probabilities of class-transition in relation with the environmental factors, and iii) prediction of the snow/no-snow labels for the cloud-obscured pixels. We validated the proposed method on daily MODIS images acquired over 10 years in a mountain area located in Italy and Austria. The proposed method yielded SCA improved maps compared to a standard method of assigning labels beneath the clouds.

A Novel Approach to Snow Coverage Retrieval Under Cloud-Obscured Pixels Based on Multitemporal Correlation / Niroumand-Jadidi, Milad; Santoni, Massimo; Bruzzone, Lorenzo; Bovolo, Francesca. - ELETTRONICO. - (2019), pp. 5730-5733. ((Intervento presentato al convegno IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium tenutosi a Yokohama, Japan nel 28th July- 2nd August 2019 [10.1109/IGARSS.2019.8899143].

A Novel Approach to Snow Coverage Retrieval Under Cloud-Obscured Pixels Based on Multitemporal Correlation

Niroumand-Jadidi, Milad;Santoni, Massimo;Bruzzone, Lorenzo;Bovolo, Francesca
2019

Abstract

This paper introduces a novel method for estimation of snow/no-snow labels for cloud-obscured pixels in order to enable an accurate mapping of the snow-covered area (SCA) in time series. The proposed method leverages the embedded information in multitemporal correlation between the presence/absence of snow and environmental factors including the topographical elevation, date of acquisition, and the cloud obscuration duration. The proposed method is built upon three main steps: i) classification of single date images into three classes (snow, no-snow, and cloud), ii) estimation of conditional probabilities of class-transition in relation with the environmental factors, and iii) prediction of the snow/no-snow labels for the cloud-obscured pixels. We validated the proposed method on daily MODIS images acquired over 10 years in a mountain area located in Italy and Austria. The proposed method yielded SCA improved maps compared to a standard method of assigning labels beneath the clouds.
IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium
USA
IEEE
978-1-5386-9154-0
Niroumand-Jadidi, Milad; Santoni, Massimo; Bruzzone, Lorenzo; Bovolo, Francesca
A Novel Approach to Snow Coverage Retrieval Under Cloud-Obscured Pixels Based on Multitemporal Correlation / Niroumand-Jadidi, Milad; Santoni, Massimo; Bruzzone, Lorenzo; Bovolo, Francesca. - ELETTRONICO. - (2019), pp. 5730-5733. ((Intervento presentato al convegno IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium tenutosi a Yokohama, Japan nel 28th July- 2nd August 2019 [10.1109/IGARSS.2019.8899143].
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11572/246076
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