The estimation of forest parameters, such as canopy height model (CHM) and above ground biomass (AGB), is of utmost importance for forest monitoring, carbon-cycle modelling, disturbance analysis, resource inventorying and natural disaster prevention. In this work, we profit from the most recent advancements in deep learning research to propose a convolutional neural network (CNN) architecture for frequent forest parameter estimation at large scale. Our technique consists of a fully convolutional, multi-modal framework, which works on a single set of complementary multi-spectral and interferometric SAR data, acquired by ESA's Sentinel-2 and DLR's TanDEM-X missions, respectively. The regression performance of our framework has been tested over four tropical forest test sites in Gabon, Africa. The estimation of CHM shows promising early results when compared to state-of-the-art methods and has the advantage of requiring only a single input image pair instead of a longer time-series, as commonly done for state-of-the-art model-based techniques.

LARGE SCALE FOREST PARAMETER ESTIMATION THROUGH A DEEP LEARNING-BASED FUSION OF SENTINEL-2 AND TANDEM-X DATA / Carcereri, D; Rizzoli, P; Ienco, D; Bueso-Bello, Jl; Gonzalez, C; Puliti, S; Bruzzone, L. - ELETTRONICO. - (2022), pp. 5773-5776. (Intervento presentato al convegno IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium tenutosi a Kuala Lumpur, Malaysia nel 17/21, Luglio 2022) [10.1109/IGARSS46834.2022.9884872].

LARGE SCALE FOREST PARAMETER ESTIMATION THROUGH A DEEP LEARNING-BASED FUSION OF SENTINEL-2 AND TANDEM-X DATA

Carcereri, D
Primo
;
Bruzzone, L
2022-01-01

Abstract

The estimation of forest parameters, such as canopy height model (CHM) and above ground biomass (AGB), is of utmost importance for forest monitoring, carbon-cycle modelling, disturbance analysis, resource inventorying and natural disaster prevention. In this work, we profit from the most recent advancements in deep learning research to propose a convolutional neural network (CNN) architecture for frequent forest parameter estimation at large scale. Our technique consists of a fully convolutional, multi-modal framework, which works on a single set of complementary multi-spectral and interferometric SAR data, acquired by ESA's Sentinel-2 and DLR's TanDEM-X missions, respectively. The regression performance of our framework has been tested over four tropical forest test sites in Gabon, Africa. The estimation of CHM shows promising early results when compared to state-of-the-art methods and has the advantage of requiring only a single input image pair instead of a longer time-series, as commonly done for state-of-the-art model-based techniques.
2022
IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium
345 E 47TH ST, NEW YORK, NY 10017 USA
IEEE
978-1-6654-2792-0
Carcereri, D; Rizzoli, P; Ienco, D; Bueso-Bello, Jl; Gonzalez, C; Puliti, S; Bruzzone, L
LARGE SCALE FOREST PARAMETER ESTIMATION THROUGH A DEEP LEARNING-BASED FUSION OF SENTINEL-2 AND TANDEM-X DATA / Carcereri, D; Rizzoli, P; Ienco, D; Bueso-Bello, Jl; Gonzalez, C; Puliti, S; Bruzzone, L. - ELETTRONICO. - (2022), pp. 5773-5776. (Intervento presentato al convegno IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium tenutosi a Kuala Lumpur, Malaysia nel 17/21, Luglio 2022) [10.1109/IGARSS46834.2022.9884872].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/386409
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