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Deep Learning for Mapping Forests with TanDEM-X

Bueso Bello, Jose Luis and Carcereri, Daniel and Gonzalez, Carolina and Martone, Michele and Rizzoli, Paola (2022) Deep Learning for Mapping Forests with TanDEM-X. In: Proceedings of the European Conference on Synthetic Aperture Radar, EUSAR. VDE Verlag GmbH. European Conference on Synthetic Aperture Radar (EUSAR), 2022-07-25 - 2022-07-27, Leipzig, Germany. ISSN 2197-4403.

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Abstract

In a bistatic SAR system such as TanDEM-X, characterized by the absence of temporal decorrelation, the interferometric coherence adds valuable information to the common amplitude images, typically acquired by monostatic SAR systems. The interferometric SAR dataset, acquired to generate the TanDEM-X global Digital Elevation Model (DEM), represents a unique data source to derive land classification maps at global scale, such as the TanDEM-X Forest/Non-Forest Map and the TanDEM-X Water Body Layer. Both maps have as main input the interferometric coherence and are based on a supervised fuzzy clustering algorithm and on the watershed segmentation algorithm, respectively. Single images are classified with the corresponding algorithm and a final weighting mosaicking strategy of overlapping coverages is necessary to improve the final accuracy of the generated classification maps. In this work, we investigate the capabilities of using a state-of-the-art convolutional neural network (CNN) with TanDEM-X interferometric data for forest and water mapping on a large scale. An ad-hoc training strategy has been developed to train a U-Net-like architecture, which aims at balancing the training data set with respect all possible acquisition geometries that can be found in TanDEM-X acquisitions. The Amazon rainforest has been used as region of interest (ROI) to compare the improvement in image classification with respect to the reference fuzzy-clustering approach. On forest classification, a significant performance improvement with respect to the clustering approach, with an f-score increase of 0.13 has been measured. This classification improvement of the forested areas, as well as the capabilities of the U-Net to accurately classify water bodies without the necessity of mosaicking overlapping acquisitions to improve the final classification accuracy, make it possible to generate up to three time-tagged mosaics over the Amazon rainforest by utilizing the nominal TanDEM-X acquisitions between 2011 and 2017.

Item URL in elib:https://elib.dlr.de/148722/
Document Type:Conference or Workshop Item (Speech)
Title:Deep Learning for Mapping Forests with TanDEM-X
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Bueso Bello, Jose LuisUNSPECIFIEDhttps://orcid.org/0000-0003-3464-2186
Carcereri, DanielUNSPECIFIEDUNSPECIFIED
Gonzalez, CarolinaUNSPECIFIEDhttps://orcid.org/0000-0002-9340-1887
Martone, MicheleUNSPECIFIEDhttps://orcid.org/0000-0002-4601-6599
Rizzoli, PaolaUNSPECIFIEDhttps://orcid.org/0000-0001-9118-2732
Date:July 2022
Journal or Publication Title:Proceedings of the European Conference on Synthetic Aperture Radar, EUSAR
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:No
Publisher:VDE Verlag GmbH
ISSN:2197-4403
Status:Published
Keywords:Synthetic Aperture Radar, TanDEM-X, rainforest, tropical forest, forest mapping, deforestation monitoring, deep learning, convolutional neural network
Event Title:European Conference on Synthetic Aperture Radar (EUSAR)
Event Location:Leipzig, Germany
Event Type:international Conference
Event Dates:2022-07-25 - 2022-07-27
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 - Support TerraSAR-X/TanDEM-X operations
Location: Oberpfaffenhofen
Institutes and Institutions:Microwaves and Radar Institute
Microwaves and Radar Institute > Spaceborne SAR Systems
Deposited By: Bueso Bello, Jose Luis
Deposited On:09 Feb 2022 06:11
Last Modified:21 Nov 2022 06:26

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