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

doi:10.1109/IGARSS47720.2021.9554689

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 proceeding › Conference contribution › peer-review

23 Citations (Scopus)

21 Downloads (Pure)

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 language	English
Title of host publication	2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS
Publisher	IEEE
Pages	3753-3756
Number of pages	4
ISBN (Electronic)	978-1-6654-0369-6, 978-1-6654-0368-9
ISBN (Print)	978-1-6654-4762-1
DOIs	https://doi.org/10.1109/IGARSS47720.2021.9554689
Publication status	Published (in print/issue) - 12 Oct 2021
Event	IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium - Brussels, Belgium Duration: 11 Jul 2021 → 16 Jul 2021

Conference

Conference	IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium
Period	11/07/21 → 16/07/21

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/IGARSS47720.2021.9554689

DEFORESTATION DETECTION BASED ON U-NET AND LSTM IN OPTICAL SATELLITE REMOTE SENSING IMAGES - acceptedAuthor Accepted Manuscript, 359 KBLicence: Unspecified

Cite this

@inproceedings{f453ddc9041a4c8babc6ef0405c5eec9,

title = "Deforestation Detection Based on U-Net and LSTM in Optical Satellite Remote Sensing Images",

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.",

author = "Jie Zhang and Zhibao Wang and Lu Bai and Guangfu Song and Jinhua Tao and Liangfu Chen",

year = "2021",

month = oct,

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doi = "10.1109/IGARSS47720.2021.9554689",

language = "English",

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booktitle = "2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS",

publisher = "IEEE",

address = "United States",

note = "IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium ; Conference date: 11-07-2021 Through 16-07-2021",

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Zhang, J, Wang, Z, Bai, L, Song, G, Tao, J & Chen, L 2021, Deforestation Detection Based on U-Net and LSTM in Optical Satellite Remote Sensing Images. in 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS. IEEE, pp. 3753-3756, IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium, 11/07/21. https://doi.org/10.1109/IGARSS47720.2021.9554689

TY - GEN

T1 - Deforestation Detection Based on U-Net and LSTM in Optical Satellite Remote Sensing Images

AU - Zhang, Jie

AU - Wang, Zhibao

AU - Bai, Lu

AU - Song, Guangfu

AU - Tao, Jinhua

AU - Chen, Liangfu

PY - 2021/10/12

Y1 - 2021/10/12

N2 - 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.

AB - 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.

UR - https://ieeexplore.ieee.org/document/9554689/

U2 - 10.1109/IGARSS47720.2021.9554689

DO - 10.1109/IGARSS47720.2021.9554689

M3 - Conference contribution

SN - 978-1-6654-4762-1

SP - 3753

EP - 3756

BT - 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS

PB - IEEE

T2 - IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium

Y2 - 11 July 2021 through 16 July 2021

ER -

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

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UN SDGs

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