We propose a system for detecting clear-cuts in Sentinel-2 (S-2) images of the Indonesian forest by means of an adaptive and unsupervised multivariate Change Vector Analysis (CVA) method. By leveraging on the unique spatial and spectral characteristics of the S- 2 mission, the proposed method characterizes a relevant portion of the target change as lying in a Gaussian neighborhood of the spectral stacked bi-temporal domain of the change. The processing system analyzes all the available bi-temporal pairs in the time series, enabling us to: (1) partially recovering lost information due to cloud coverage, and (2) providing a representation of the change evolving in time. The system is fully automated and potentially operational ready, so it can be used to provide accurate information about clear-cuts at the country scale in Indonesia.

A Multivariate Change Vector Analysis System for Unsupervised Detection of Clear-Cuts in Sentinel-2 Time Series of the Indonesian Forest / Zanetti, Massimo; Bruzzone, Lorenzo; Fernandez-Prieto, Diego. - (2018), pp. 1942-1945. (Intervento presentato al convegno IGARSS 2018 tenutosi a Valencia nel 22nd–27th July 2018) [10.1109/IGARSS.2018.8519185].

A Multivariate Change Vector Analysis System for Unsupervised Detection of Clear-Cuts in Sentinel-2 Time Series of the Indonesian Forest

Zanetti, Massimo;Bruzzone, Lorenzo;
2018-01-01

Abstract

We propose a system for detecting clear-cuts in Sentinel-2 (S-2) images of the Indonesian forest by means of an adaptive and unsupervised multivariate Change Vector Analysis (CVA) method. By leveraging on the unique spatial and spectral characteristics of the S- 2 mission, the proposed method characterizes a relevant portion of the target change as lying in a Gaussian neighborhood of the spectral stacked bi-temporal domain of the change. The processing system analyzes all the available bi-temporal pairs in the time series, enabling us to: (1) partially recovering lost information due to cloud coverage, and (2) providing a representation of the change evolving in time. The system is fully automated and potentially operational ready, so it can be used to provide accurate information about clear-cuts at the country scale in Indonesia.
2018
2018 IEEE International Geoscience and Remote Sensing Symposium: Observing, Understanding And Forecasting The Dynamics Of Our Planet
Piscataway, USA
IEEE
978-1-5386-7150-4
Zanetti, Massimo; Bruzzone, Lorenzo; Fernandez-Prieto, Diego
A Multivariate Change Vector Analysis System for Unsupervised Detection of Clear-Cuts in Sentinel-2 Time Series of the Indonesian Forest / Zanetti, Massimo; Bruzzone, Lorenzo; Fernandez-Prieto, Diego. - (2018), pp. 1942-1945. (Intervento presentato al convegno IGARSS 2018 tenutosi a Valencia nel 22nd–27th July 2018) [10.1109/IGARSS.2018.8519185].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/225753
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