Detecting clearcut deforestation employing deep learning methods and SAR time series

Evandro C. Taquary, L.M.G. Fonseca, R.V. Maretto, Hugo N. Bendini, Bruno M. Matosak, Sidnei J. S. Sant'Anna, José C. Mura

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

Automating the systematic monitoring of deforestation in the Brazilian biomes has become imperative. In this sense, a promising research field lies upon the exploitation of orbital imaging based on Synthetic Aperture Radar (SAR) sensors, since this technology is less affected by cloud cover, allowing systematic data acquisitions. In addition, the growing availability of with no charge SAR data products enables investigations on the use of time series extracted from this category of instruments, paving the way for more sophisticated temporal analyzes. This work presents the results of a SAR time series classification model designed to identify clearcut deforestation patterns in time, through an Artificial Intelligence approach known as Recurrent Neural Networks. The classification was performed using 5216 samples of Sentinel-1 time series within the Amazon basin, reaching an overall accuracy of 96.74%.
Original languageEnglish
Title of host publication2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS
PublisherIEEE
Pages4520-4523
Number of pages4
ISBN (Electronic)978-1-6654-0369-6
ISBN (Print)978-1-6654-4762-1
DOIs
Publication statusPublished - 12 Oct 2021
EventIEEE- International Geoscience and Remote Sensing Symposium- IGARSS 2021 - Brussels, Virtual Conference
Duration: 12 Jul 202116 Jul 2021
https://igarss2021.com

Conference

ConferenceIEEE- International Geoscience and Remote Sensing Symposium- IGARSS 2021
CityVirtual Conference
Period12/07/2116/07/21
Internet address

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