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Deep Learning for Mapping the Amazon Rainforest with TanDEM-X

Bueso Bello, Jose Luis and Pulella, Andrea and Sica, Francescopaolo and Rizzoli, Paola (2021) Deep Learning for Mapping the Amazon Rainforest with TanDEM-X. In: International Geoscience and Remote Sensing Symposium (IGARSS). International Geoscience and Remote Sensing Symposium (IGARSS), 2021-07-12 - 2021-07-16, Brussels, Belgium. doi: 10.1109/IGARSS47720.2021.9554536.

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Abstract

The TanDEM-X Synthetic Aperture Radar (SAR) system allows for the recording of the bistatic interferometric coherence, which adds additional information to the common amplitude images acquired by monostatic SAR systems. More concretely, the volume decorrelation factor, which influences the interferometric coherence, has been proved to be a reliable indicator of vegetated areas and was exploited in [1] to generate the global TanDEM-X Forest/Non-Forest Map, based on a supervised clustering algorithm. In this work, we investigate ad-hoc training strategies to extent the Convolutional Neural Network (CNN) presented in [2] for mapping forests and monitoring the extend of the Amazonas using TanDEM-X. By applying the proposed method on single TanDEM-X images, we achieved a significant performance improvement with respect to the clustering approach, with an f-score increase of 0.13, using as reference a forest map of 2010 based on Landsat data. The improvement in the forest classification makes it possible to skip the weighted mosaicking of overlapping images used in the clustering approach for achieving a good final accuracy. In this way, we were able to generate three time-tagged mosaics over the Amazon rainforest, by utilizing the nominal TanDEM-X acquisitions between 2011 and 2017. In the final paper, we will present more consolidated results, including the validation and comparison of the generated mosaics, as well as change detection investigations, aimed at showing the capabilities of Deep Learning approaches for forest mapping and monitoring with bistatic TanDEM-X images.

Item URL in elib:https://elib.dlr.de/141565/
Document Type:Conference or Workshop Item (Speech)
Title:Deep Learning for Mapping the Amazon Rainforest with TanDEM-X
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Bueso Bello, Jose LuisUNSPECIFIEDhttps://orcid.org/0000-0003-3464-2186UNSPECIFIED
Pulella, AndreaUNSPECIFIEDhttps://orcid.org/0000-0001-6295-617XUNSPECIFIED
Sica, FrancescopaoloUNSPECIFIEDhttps://orcid.org/0000-0003-1593-1492UNSPECIFIED
Rizzoli, PaolaUNSPECIFIEDhttps://orcid.org/0000-0001-9118-2732UNSPECIFIED
Date:July 2021
Journal or Publication Title:International Geoscience and Remote Sensing Symposium (IGARSS)
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:No
DOI:10.1109/IGARSS47720.2021.9554536
Status:Published
Keywords:Synthetic Aperture Radar, TanDEM-X, Amazon, forest mapping, deforestation monitoring, deep learning, convolutional neural network
Event Title:International Geoscience and Remote Sensing Symposium (IGARSS)
Event Location:Brussels, Belgium
Event Type:international Conference
Event Dates:2021-07-12 - 2021-07-16
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 - Projekt TanDEM-X (old)
Location: Oberpfaffenhofen
Institutes and Institutions:Microwaves and Radar Institute
Microwaves and Radar Institute > Spaceborne SAR Systems
Deposited By: Bueso Bello, Jose Luis
Deposited On:26 Mar 2021 16:36
Last Modified:28 Feb 2024 14:52

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