Potential of Convolutional Neural Networks for Forest Mapping Using Sentinel-1 Interferometric Short Time Series

Diniz Dal Molin Junior, Ricardo Simao and Rizzoli, Paola (2022) Potential of Convolutional Neural Networks for Forest Mapping Using Sentinel-1 Interferometric Short Time Series. Remote Sensing, 14 (6). Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/rs14061381. ISSN 2072-4292.

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Item URL in elib:

https://elib.dlr.de/192483/

Document Type:

Article

Title:

Potential of Convolutional Neural Networks for Forest Mapping Using Sentinel-1 Interferometric Short Time Series

Authors:

Authors	Institution or Email of Authors	Author's ORCID iD	ORCID Put Code
Diniz Dal Molin Junior, Ricardo Simao	UNSPECIFIED	https://orcid.org/0000-0003-4103-7910	UNSPECIFIED
Rizzoli, Paola	UNSPECIFIED	https://orcid.org/0000-0001-9118-2732	UNSPECIFIED

Date:

12 March 2022

Journal or Publication Title:

Remote Sensing

Refereed publication:

Yes

Open Access:

Yes

Gold Open Access:

Yes

In SCOPUS:

Yes

In ISI Web of Science:

Yes

Volume:

DOI:

10.3390/rs14061381

Publisher:

Multidisciplinary Digital Publishing Institute (MDPI)

ISSN:

2072-4292

Status:

Published

Keywords:

Synthetic Aperture Radar; Sentinel-1; forest mapping; deforestation monitoring; deep learning; convolutional neural networks

HGF - Research field:

Aeronautics, Space and Transport

HGF - Program:

Space

HGF - Program Themes:

Earth Observation

DLR - Research area:

Raumfahrt

DLR - Program:

R EO - Earth Observation

DLR - Research theme (Project):

R - AI4SAR

Location:

Oberpfaffenhofen

Institutes and Institutions:

Microwaves and Radar Institute
Microwaves and Radar Institute > Spaceborne SAR Systems

Deposited By:

Diniz Dal Molin Junior, Ricardo Simao

Deposited On:

19 Dec 2022 06:16

Last Modified:

19 Oct 2023 13:39

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