Semi-/Weakly-Supervised Semantic Segmentation Method and Its Application for Coastal Aquaculture Areas Based on Multi-Source Remote Sensing Images-Taking the Fujian Coastal Area (Mainly Sanduo) as an Example

doi:10.3390/rs13061083

	Semi-/Weakly-Supervised Semantic Segmentation Method and Its Application for Coastal Aquaculture Areas Based on Multi-Source Remote Sensing Images-Taking the Fujian Coastal Area (Mainly Sanduo) as an Example
	Liang, Chenbin 1,2,3; Cheng, Bo 1; Xiao, Baihua ; He, Chenlinqiu 1,4; Liu, Xunan 5; Jia, Ning 5; Chen, Jinfen 1,4
刊名	REMOTE SENSING
	2021-03-01
卷号	13 期号:6 页码:21
关键词	coastal aquaculture areas semantic segmentation semi- weakly-supervised learning GAN conditional adversarial learning
DOI	10.3390/rs13061083
通讯作者	Cheng, Bo(chengbo@aircas.ac.cn)
英文摘要	Coastal aquaculture areas are some of the main areas to obtain marine fishery resources and are vulnerable to storm-tide disasters. Obtaining the information of coastal aquaculture areas quickly and accurately is important for the scientific management and planning of aquaculture resources. Recently, deep neural networks have been widely used in remote sensing to deal with many problems, such as scene classification and object detection, and there are many data sources with different spatial resolutions and different uses with the development of remote sensing technology. Thus, using deep learning networks to extract coastal aquaculture areas often encounters the following problems: (1) the difficulty in labeling; (2) the poor robustness of the model; (3) the spatial resolution of the image to be processed is inconsistent with that of the existing samples. In order to fix these problems, this paper proposes a novel semi-/weakly-supervised method, the semi-/weakly-supervised semantic segmentation network (Semi-SSN), and adopts 3 data sources: GaoFen-2 image, GaoFen-1(PMS)image, and GanFen-1(WFV)image with a 0.8 m, 2 m, and 16 m spatial resolution, respectively, and through experiments, we analyze the extraction effect of the model comprehensively. After comparing with other the-state-of-art methods and verifying on an open remote sensing dataset, we take the Fujian coastal area (mainly Sanduo) as the experimental area and employ our method to detect the effect of storm-tide disasters on coastal aquaculture areas, monitor the production, and make the distribution map of coastal aquaculture areas.
资助项目	National Natural Science Foundation of China[61731022] ; National Natural Science Foundation of China[61531019]
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
语种	英语
出版者	MDPI
WOS记录号	WOS:000651966700001
资助机构	National Natural Science Foundation of China
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/45194]
专题	自动化研究所_类脑智能研究中心
通讯作者	Cheng, Bo
作者单位	1.Chinese Acad Sci, Aerosp Informat Res Inst, Beijing 100094, Peoples R China 2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100190, Peoples R China 4.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100190, Peoples R China 5.Natl Marine Hazard Mitigat Serv, Beijing 100194, Peoples R China
推荐引用方式 GB/T 7714	Liang, Chenbin,Cheng, Bo,Xiao, Baihua,et al. Semi-/Weakly-Supervised Semantic Segmentation Method and Its Application for Coastal Aquaculture Areas Based on Multi-Source Remote Sensing Images-Taking the Fujian Coastal Area (Mainly Sanduo) as an Example[J]. REMOTE SENSING,2021,13(6):21.
APA	Liang, Chenbin.,Cheng, Bo.,Xiao, Baihua.,He, Chenlinqiu.,Liu, Xunan.,...&Chen, Jinfen.(2021).Semi-/Weakly-Supervised Semantic Segmentation Method and Its Application for Coastal Aquaculture Areas Based on Multi-Source Remote Sensing Images-Taking the Fujian Coastal Area (Mainly Sanduo) as an Example.REMOTE SENSING,13(6),21.
MLA	Liang, Chenbin,et al."Semi-/Weakly-Supervised Semantic Segmentation Method and Its Application for Coastal Aquaculture Areas Based on Multi-Source Remote Sensing Images-Taking the Fujian Coastal Area (Mainly Sanduo) as an Example".REMOTE SENSING 13.6(2021):21.