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Deep Learning for Mapping Tropical Forests with TanDEM-X Bistatic InSAR Data

Bueso Bello, Jose Luis and Carcereri, Daniel and Martone, Michele and Gonzalez, Carolina and Posovszky, Philipp and Rizzoli, Paola (2022) Deep Learning for Mapping Tropical Forests with TanDEM-X Bistatic InSAR Data. Remote Sensing, 14 (3981). Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/rs14163981. ISSN 2072-4292.

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Official URL: https://www.mdpi.com/2072-4292/14/16/3981


The TanDEM-X synthetic aperture radar (SAR) system allows for the recording of bistatic interferometric SAR (InSAR) acquisitions, which provide additional information to the common amplitude images acquired by monostatic SAR systems. More concretely, the volume decorrelation factor, which can be derived from the bistatic interferometric coherence, is a reliable indicator of the presence of vegetation and it was used as main input feature for the generation of the global TanDEM-X forest/non-forest map, by means of a clustering algorithm. In this work, we investigate the capabilities of deep Convolutional Neural Networks (CNNs) for mapping tropical forests at large-scale using TanDEM-X InSAR data. For this purpose, we rely on a U-Net architecture, which takes as input a set of feature maps selected on the basis of previous preparatory works. Moreover, we design an ad hoc training strategy, aimed at developing a robust model for global mapping purposes, which has to properly manage the large variety of different acquisition geometries characterizing the TanDEM-X global data set. In addition to detecting forest/non-forest areas, the CNN has also been trained to detect water surfaces, which are typically characterized by low values of coherence. By applying the proposed method on single TanDEM-X images, we achieved a significant performance improvement with respect to the baseline clustering approach, with an average F-score increase of 0.13. We then applied such a model for mapping the entire Amazon rainforest, as well as the other tropical forests in Central Africa and South-East Asia, in order to test its robustness and generalization capabilities, and we observed that forests are typically well detected as contour closed regions and that water classification is reliable, too. Finally, the generated maps show a great potential for mapping temporal changes occurring over forested areas and can be used for generating large-scale maps of deforestation.

Item URL in elib:https://elib.dlr.de/190328/
Document Type:Article
Title:Deep Learning for Mapping Tropical Forests with TanDEM-X Bistatic InSAR Data
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Bueso Bello, Jose LuisUNSPECIFIEDhttps://orcid.org/0000-0003-3464-2186UNSPECIFIED
Martone, MicheleUNSPECIFIEDhttps://orcid.org/0000-0002-4601-6599UNSPECIFIED
Gonzalez, CarolinaUNSPECIFIEDhttps://orcid.org/0000-0002-9340-1887UNSPECIFIED
Posovszky, PhilippUNSPECIFIEDhttps://orcid.org/0000-0003-0656-3691UNSPECIFIED
Rizzoli, PaolaUNSPECIFIEDhttps://orcid.org/0000-0001-9118-2732UNSPECIFIED
Date:16 August 2022
Journal or Publication Title:Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In ISI Web of Science:Yes
Publisher:Multidisciplinary Digital Publishing Institute (MDPI)
Keywords:synthetic aperture radar; forest mapping; deforestation monitoring; deep learning; convolutional neural networks; TanDEM-X
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:21 Nov 2022 06:33
Last Modified:21 Nov 2022 06:33

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