Mapping inundation extents in Poyang Lake area using Sentinel-1 data and transformer-based change detection method
(2023) In Journal of Hydrology 620.- Abstract
Accurate and timely mapping of inundation extents during flood periods is essential for disaster evaluation and development of rescue strategies. With unique advantages over the optical sensors (e.g., little effect of clouds, and observations at day and night), Synthetic aperture radar (SAR) sensors provide an important data source for mapping inundation, particularly during flood periods. Freely available SAR images from Sentinel-1 have been increasingly used for many applications. This study applied an efficient transformer-based change detection method, bitemporal image transformer (BiT) with bitemporal Sentniel-1 images, to map inundation extents and evolution in Poyang Lake area in 2020. The transformer-based change detection... (More)
Accurate and timely mapping of inundation extents during flood periods is essential for disaster evaluation and development of rescue strategies. With unique advantages over the optical sensors (e.g., little effect of clouds, and observations at day and night), Synthetic aperture radar (SAR) sensors provide an important data source for mapping inundation, particularly during flood periods. Freely available SAR images from Sentinel-1 have been increasingly used for many applications. This study applied an efficient transformer-based change detection method, bitemporal image transformer (BiT) with bitemporal Sentniel-1 images, to map inundation extents and evolution in Poyang Lake area in 2020. The transformer-based change detection method firstly adopted ResNet for high-level semantic features extraction, and applied a transformer mechanism to refine these features pixel-wise, followed by employing a FCN as the prediction head for generating the results of change detection. Besides, we constructed a water change detection dataset with spatial-and-temporal generalization from bitemporal Sentinel-1 images; this dataset consists of the seasonal variation water samples of Poyang Lake for years. We compared the results from the BiT method with other convolutional neural network (CNN) based methods (STANets and SNUNet). Mapped inundation extents were evaluated with the ground truth visually derived from high spatial resolution images. The evaluation showed the BiT method generated high accurate mapped inundation extents with the F1-score of 95.5%. The BiT model has proven its superior performance in detecting increased water. Based on the results of the BiT method, the variation of inundation extents in Poyang Lake during May-November 2020 was further analyzed. It was found that the water surface coverage of Poyang Lake is the smallest in late May; it gradually increased to the maximum on 14th July, and then began to stabilize and show a significant downward trend before November. The flood distribution map shows that cultivated land has been inundated with the largest area of approximately 600 km2.
(Less)
- author
- Dong, Zhen ; Liang, Zifan ; Wang, Guojie ; Amankwah, Solomon Obiri Yeboah ; Feng, Donghan ; Wei, Xikun and Duan, Zheng LU
- organization
- publishing date
- 2023
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Change detection, Deep learning, Flood monitoring, Poyang Lake, SAR, Transformer
- in
- Journal of Hydrology
- volume
- 620
- article number
- 129455
- publisher
- Elsevier
- external identifiers
-
- scopus:85151484836
- ISSN
- 0022-1694
- DOI
- 10.1016/j.jhydrol.2023.129455
- language
- English
- LU publication?
- yes
- id
- 2686cb13-75af-492a-8710-6bd3c7d419fb
- date added to LUP
- 2023-05-16 15:41:04
- date last changed
- 2024-05-27 15:00:20
@article{2686cb13-75af-492a-8710-6bd3c7d419fb,
abstract = {{<p>Accurate and timely mapping of inundation extents during flood periods is essential for disaster evaluation and development of rescue strategies. With unique advantages over the optical sensors (e.g., little effect of clouds, and observations at day and night), Synthetic aperture radar (SAR) sensors provide an important data source for mapping inundation, particularly during flood periods. Freely available SAR images from Sentinel-1 have been increasingly used for many applications. This study applied an efficient transformer-based change detection method, bitemporal image transformer (BiT) with bitemporal Sentniel-1 images, to map inundation extents and evolution in Poyang Lake area in 2020. The transformer-based change detection method firstly adopted ResNet for high-level semantic features extraction, and applied a transformer mechanism to refine these features pixel-wise, followed by employing a FCN as the prediction head for generating the results of change detection. Besides, we constructed a water change detection dataset with spatial-and-temporal generalization from bitemporal Sentinel-1 images; this dataset consists of the seasonal variation water samples of Poyang Lake for years. We compared the results from the BiT method with other convolutional neural network (CNN) based methods (STANets and SNUNet). Mapped inundation extents were evaluated with the ground truth visually derived from high spatial resolution images. The evaluation showed the BiT method generated high accurate mapped inundation extents with the F1-score of 95.5%. The BiT model has proven its superior performance in detecting increased water. Based on the results of the BiT method, the variation of inundation extents in Poyang Lake during May-November 2020 was further analyzed. It was found that the water surface coverage of Poyang Lake is the smallest in late May; it gradually increased to the maximum on 14th July, and then began to stabilize and show a significant downward trend before November. The flood distribution map shows that cultivated land has been inundated with the largest area of approximately 600 km<sup>2</sup>.</p>}},
author = {{Dong, Zhen and Liang, Zifan and Wang, Guojie and Amankwah, Solomon Obiri Yeboah and Feng, Donghan and Wei, Xikun and Duan, Zheng}},
issn = {{0022-1694}},
keywords = {{Change detection; Deep learning; Flood monitoring; Poyang Lake; SAR; Transformer}},
language = {{eng}},
publisher = {{Elsevier}},
series = {{Journal of Hydrology}},
title = {{Mapping inundation extents in Poyang Lake area using Sentinel-1 data and transformer-based change detection method}},
url = {{http://dx.doi.org/10.1016/j.jhydrol.2023.129455}},
doi = {{10.1016/j.jhydrol.2023.129455}},
volume = {{620}},
year = {{2023}},
}