气候变化领域集成服务门户(CCPortal): Deep Transformer-Based Network Deforestation Detection in the Brazilian Amazon Using Sentinel-2 Imagery

CCPortal

DOI	10.1109/LGRS.2024.3355104
	Deep Transformer-Based Network Deforestation Detection in the Brazilian Amazon Using Sentinel-2 Imagery
	Alshehri, Mariam; Ouadou, Anes; Scott, Grant J.
发表日期	2024
ISSN	1545-598X
EISSN	1558-0571
起始页码	21
卷号	21
英文摘要	Deforestation poses a critical environmental challenge with far-reaching impacts on climate change, biodiversity, and local communities. As such, detecting and monitoring deforestation are crucial, and recent advancements in deep learning (DL) and remote sensing technologies offer a promising solution to this challenge. In this study, we adapt ChangeFormer, a transformer-based framework, to detect deforestation in the Brazilian Amazon, employing the attention mechanism to analyze spatial and temporal patterns in bitemporal satellite images. To assess the model's effectiveness, we employed a robust approach to create a deforestation detection (DD) dataset, utilizing Sentinel-2 imagery from select conservation areas in the Brazilian Amazon throughout 2020 and 2021. Our dataset comprises 7734 pairs of bitemporal image chips with a resolution of 256 x 256 pixels and 1406 pairs of image chips with a resolution of 512 x 512 pixels. The model achieved an overall accuracy (OA) of 93% with a corresponding F1 score of 90% and an intersection over union (IoU) score of 82%. These results demonstrate the potential of transformer-based networks for accurate and efficient DD.
英文关键词	Climate change; Environmental monitoring; Deforestation; Forestry; Change detection algorithms; Deep learning; Transformers; Biodiversity; Detection algorithms; Spatiotemporal phenomena; Satellite images; South America; Change detection (CD); deep learning (DL); deforestation; transformer
语种	英语
WOS研究方向	Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Geochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:001230653000023
来源期刊	IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/287676
作者单位	University of Missouri System; University of Missouri Columbia; Princess Nourah bint Abdulrahman University
推荐引用方式 GB/T 7714	Alshehri, Mariam,Ouadou, Anes,Scott, Grant J.. Deep Transformer-Based Network Deforestation Detection in the Brazilian Amazon Using Sentinel-2 Imagery[J],2024,21.
APA	Alshehri, Mariam,Ouadou, Anes,&Scott, Grant J..(2024).Deep Transformer-Based Network Deforestation Detection in the Brazilian Amazon Using Sentinel-2 Imagery.IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,21.
MLA	Alshehri, Mariam,et al."Deep Transformer-Based Network Deforestation Detection in the Brazilian Amazon Using Sentinel-2 Imagery".IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 21(2024).