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/ | ||||||||||||||||||||
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Document Type: | Conference or Workshop Item (Speech) | ||||||||||||||||||||
Title: | Deep Learning for Mapping the Amazon Rainforest with TanDEM-X | ||||||||||||||||||||
Authors: |
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Date: | July 2021 | ||||||||||||||||||||
Journal or Publication Title: | International Geoscience and Remote Sensing Symposium (IGARSS) | ||||||||||||||||||||
Refereed publication: | Yes | ||||||||||||||||||||
Open Access: | No | ||||||||||||||||||||
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: | 17 Jul 2023 12:58 |
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