气候变化领域集成服务门户(CCPortal): HA-Net: A Lake Water Body Extraction Network Based on Hybrid-Scale Attention and Transfer Learning

CCPortal

DOI	10.3390/rs13204121
	HA-Net: A Lake Water Body Extraction Network Based on Hybrid-Scale Attention and Transfer Learning
	Wang, Zhaobin; Gao, Xiong; Zhang, Yaonan
通讯作者	Wang, ZB (通讯作者)，Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou 730000, Peoples R China.
发表日期	2021
EISSN	2072-4292
卷号	13 期号:20
英文摘要	Due to the large quantity of noise and complex spatial background of the remote sensing images, how to improve the accuracy of semantic segmentation has become a hot topic. Lake water body extraction is crucial for disaster detection, resource utilization, and carbon cycle, etc. The the area of lakes on the Tibetan Plateau has been constantly changing due to the movement of the Earth's crust. Most of the convolutional neural networks used for remote sensing images are based on single-layer features for pixel classification while ignoring the correlation of such features in different layers. In this paper, the two-branch encoder is presented, which is a multiscale structure that combines the features of ResNet-34 with a feature pyramid network. Secondly, adaptive weights are distributed to global information using the hybrid-scale attention block. Finally, PixelShuffle is used to recover the feature maps' resolution, and the densely connected block is used to refine the boundary of the lake water body. Likewise, we transfer the best weights which are saved on the Google dataset to the Landsat-8 dataset to ensure that our proposed method is robust. We validate the superiority of Hybrid-scale Attention Network (HA-Net) on two given datasets, which were created by us using Google and Landsat-8 remote sensing images. (1) On the Google dataset, HA-Net achieves the best performance of all five evaluation metrics with a Mean Intersection over Union (MIoU) of 97.38%, which improves by 1.04% compared with DeepLab V3+, and reduces the training time by about 100 s per epoch. Moreover, the overall accuracy (OA), Recall, True Water Rate (TWR), and False Water Rate (FWR) of HA-Net are 98.88%, 98.03%, 98.24%, and 1.76% respectively. (2) On the Landsat-8 dataset, HA-Net achieves the best overall accuracy and the True Water Rate (TWR) improvement of 2.93% compared to Pre_PSPNet, which proves to be more robust than other advanced models.
关键词	INDEX NDWI SEGMENTATION DELINEATION FEATURES
英文关键词	deep convolutional neural network; remote sensing image; semantic segmentation; tibetan plateau; transfer learning; attention mechanism
语种	英语
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:000713986000001
来源期刊	REMOTE SENSING
来源机构	中国科学院西北生态环境资源研究院
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/254308
作者单位	[Wang, Zhaobin; Gao, Xiong] Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou 730000, Peoples R China; [Zhang, Yaonan] Chinese Acad Sci, Natl Glaciol Geocryol Desert Data Ctr, Northwest Inst Ecoenvironm & Resources, Lanzhou 730000, Peoples R China
推荐引用方式 GB/T 7714	Wang, Zhaobin,Gao, Xiong,Zhang, Yaonan. HA-Net: A Lake Water Body Extraction Network Based on Hybrid-Scale Attention and Transfer Learning[J]. 中国科学院西北生态环境资源研究院,2021,13(20).
APA	Wang, Zhaobin,Gao, Xiong,&Zhang, Yaonan.(2021).HA-Net: A Lake Water Body Extraction Network Based on Hybrid-Scale Attention and Transfer Learning.REMOTE SENSING,13(20).
MLA	Wang, Zhaobin,et al."HA-Net: A Lake Water Body Extraction Network Based on Hybrid-Scale Attention and Transfer Learning".REMOTE SENSING 13.20(2021).