Deforestation Detection Based on U-Net and LSTM in Optical Satellite Remote Sensing Images

Jie Zhang, Zhibao Wang, Lu Bai, Guangfu Song, Jinhua Tao, Liangfu Chen

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

Abstract

The protection and monitoring of forest resources has drawn considerable national attention. Traditional deforestation monitoring requires a lot of manpower and material resources through manual visual interpretation and manual change patterns labelling, which has problems of low efficiency and high missed alarm rate. Therefore, this paper explores the detection for deforestation changes from remote sensing images based on deep learning framework, and aims to help forestry department manage and monitor forest resources. In this paper, an U-Net+LSTM framework is used to detect the changes of deforestation from remote sensing images. The evaluation data is Sentinel-2 dataset and the study area is Guangxi Sanjiang Dong Autonomous County in China. The results show that the F1 score of the framework is as high as 0.715, which proves the proposed model can effectively detect the change from forest to bare soil in remote sensing images.
Original languageEnglish
Title of host publication2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS
PublisherIEEE
Pages3753-3756
Number of pages4
ISBN (Electronic)978-1-6654-0369-6, 978-1-6654-0368-9
ISBN (Print)978-1-6654-4762-1
DOIs
Publication statusPublished - 12 Oct 2021
EventIGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium - Brussels, Belgium
Duration: 11 Jul 202116 Jul 2021

Conference

ConferenceIGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium
Period11/07/2116/07/21

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