Deep learning classification of coastal wetland hyperspectral image combined spectra and texture features: A case study of Huanghe (Yellow) River Estuary wetland

doi:10.1007/s13131-019-1445-z

	Deep learning classification of coastal wetland hyperspectral image combined spectra and texture features: A case study of Huanghe (Yellow) River Estuary wetland
	Hu, Yabin 1,2; Zhang, Jie 2; Ma, Yi 2; Li, Xiaomin 2; Sun, Qinpei 3; An, Jubai 1
刊名	ACTA OCEANOLOGICA SINICA
	2019-05
卷号	38 期号:5 页码:142-150
关键词	coastal wetland hyperspectral image deep learning classification
ISSN号	0253-505X
DOI	10.1007/s13131-019-1445-z
英文摘要	This paper develops a deep learning classification method with fully-connected 8-layers characteristics to classification of coastal wetland based on CHRIS hyperspectral image. The method combined spectral feature and multi-spatial texture feature information has been applied in the Huanghe (Yellow) River Estuary coastal wetland. The results show that: (1) Based on testing samples, the DCNN model combined spectral feature and texture feature after K-L transformation appear high classification accuracy, which is up to 99%. (2) The accuracy by using spectral feature with all the texture feature is lower than that using spectral only and combing spectral and texture feature after K-L transformation. The DCNN classification accuracy using spectral feature and texture feature after K-L transformation was up to 99.38%, and the outperformed that of all the texture feature by 4.15%. (3) The classification accuracy of the DCNN method achieves better performance than other methods based on the whole validation image, with an overall accuracy of 84.64% and the Kappa coefficient of 0.80. (4) The developed DCNN model classification algorithm ensured the accuracy of all types is more balanced, and it also greatly improved the accuracy of tidal flat and farmland, while kept the classification accuracy of main types almost invariant compared to the shallow algorithms. The classification accuracy of tidal flat and farmland is up to 79.26% and 56.72% respectively based on the DCNN model. And it improves by about 2.51% and 10.6% compared with that of the other shallow classification methods.
资助项目	National Natural Science Foundation of China[61601133] ; National Natural Science Foundation of China[41206172]
WOS关键词	ALGORITHM
WOS研究方向	Oceanography
语种	英语
出版者	SPRINGER
WOS记录号	WOS:000484973600014
内容类型	期刊论文
源URL	[http://ir.fio.com.cn:8080/handle/2SI8HI0U/30074]
专题	自然资源部第一海洋研究所
通讯作者	Ma, Yi
作者单位	1.Dalian Maritime Univ, Informat Sci & Technol Coll, Dalian 116026, Peoples R China 2.Minist Nat Resources, Inst Oceanog 1, Qingdao 266061, Shandong, Peoples R China 3.Shandong Univ Sci & Technol, Coll Surveying Sci & Engn, Qingdao 266590, Shandong, Peoples R China
推荐引用方式 GB/T 7714	Hu, Yabin,Zhang, Jie,Ma, Yi,et al. Deep learning classification of coastal wetland hyperspectral image combined spectra and texture features: A case study of Huanghe (Yellow) River Estuary wetland[J]. ACTA OCEANOLOGICA SINICA,2019,38(5):142-150.
APA	Hu, Yabin,Zhang, Jie,Ma, Yi,Li, Xiaomin,Sun, Qinpei,&An, Jubai.(2019).Deep learning classification of coastal wetland hyperspectral image combined spectra and texture features: A case study of Huanghe (Yellow) River Estuary wetland.ACTA OCEANOLOGICA SINICA,38(5),142-150.
MLA	Hu, Yabin,et al."Deep learning classification of coastal wetland hyperspectral image combined spectra and texture features: A case study of Huanghe (Yellow) River Estuary wetland".ACTA OCEANOLOGICA SINICA 38.5(2019):142-150.