Locally adaptive linear mixture model-based super-resolution land-cover mapping based on a structure tensor

doi:10.1080/01431161.2016.1249305

CORC > 合肥物质科学研究院 > 中国科学院合肥物质科学研究院 > 应用技术研究所

	Locally adaptive linear mixture model-based super-resolution land-cover mapping based on a structure tensor
	Li, Xiaodong 1; Du, Yun 1; Ling, Feng 1; Li, Wenbo 2
刊名	INTERNATIONAL JOURNAL OF REMOTE SENSING
	2016
卷号	37 期号:24 页码:5802-5825
DOI	10.1080/01431161.2016.1249305
文献子类	Article
英文摘要	Super-resolution land-cover mapping (SRM) is a technique for generating land-cover thematic maps with a finer spatial resolution than the input image. Linear mixture model-based SRM (LSRM) is applied directly to a remotely sensed image and is composed of a spatial term that integrates the land-cover spatial pattern prior information, a spectral term that assumes that the spectral signature of each mixed pixel is composed of a weighted linear sum of endmember spectral signatures within that pixel and a balance parameter that defines the weight of the spatial term. The traditional LSRM adopts an isotropic spatial autocorrelation model in the land-cover spatial term for different classes and a fixed balance parameter for the entire image, and ignores the image local properties. The class boundaries are at risk of over-smoothing and may be imprecise, and the homogeneous regions may be unsmoothed and contain speckle-like artefacts in the result. This study proposes a locally adaptive LSRM (LA-LSRM) that integrates image local properties to predict fine spatial resolution pixel labels. The structure tensor is applied to detect the image local information. The LA-LSRM spatial term is locally adaptive and is composed of an anisotropic spatial autocorrelation model in which the spatial autocorrelation orientations of different classes may vary. The LA-LSRM balance parameter is locally adaptive to the different regions of the image. Such parameter obtains a relatively large value when the fine-resolution pixel is located in the homogeneous region to remove speckle-like artefacts and a relatively small value when the fine-resolution pixel is at the class boundary to preserve the edge. The LA-LSRM performance was assessed using a simulated multi-spectral image, an IKONOS multi-spectral image, a hyperspectral image produced by Airborne Visible/Infrared Imaging Spectrometer and a hyperspectral image produced by reflective optics system imaging spectrometer. Results show that the homogeneous regions were smoothed, the boundaries were better preserved and the overall accuracies were increased by LA-LSRM compared with traditional LSRM in all experiments.
WOS关键词	REMOTELY-SENSED IMAGERY ; SPATIAL REGULARIZATION ; SENSING IMAGERY ; HYPERSPECTRAL IMAGERY ; MAP MODEL ; RESOLUTION ; SCALE ; INFORMATION ; SELECTION ; ACCURACY
WOS研究方向	Remote Sensing ; Imaging Science & Photographic Technology
语种	英语
WOS记录号	WOS:000388599500007
资助机构	Natural Science Foundation of China(41301398) ; Natural Science Foundation of China(41301398) ; Natural Science Foundation of China(41301398) ; Natural Science Foundation of China(41301398) ; Wuhan ChenGuang Youth Sci.Tech. Project(2014072704011254) ; Wuhan ChenGuang Youth Sci.Tech. Project(2014072704011254) ; Wuhan ChenGuang Youth Sci.Tech. Project(2014072704011254) ; Wuhan ChenGuang Youth Sci.Tech. Project(2014072704011254) ; National Basic Research Program (973 Program) of China(2013cb733205) ; National Basic Research Program (973 Program) of China(2013cb733205) ; National Basic Research Program (973 Program) of China(2013cb733205) ; National Basic Research Program (973 Program) of China(2013cb733205) ; National Key Technologies Research and Development Program of China(2016YFB0502604) ; National Key Technologies Research and Development Program of China(2016YFB0502604) ; National Key Technologies Research and Development Program of China(2016YFB0502604) ; National Key Technologies Research and Development Program of China(2016YFB0502604) ; Natural Science Foundation of China(41301398) ; Natural Science Foundation of China(41301398) ; Natural Science Foundation of China(41301398) ; Natural Science Foundation of China(41301398) ; Wuhan ChenGuang Youth Sci.Tech. Project(2014072704011254) ; Wuhan ChenGuang Youth Sci.Tech. Project(2014072704011254) ; Wuhan ChenGuang Youth Sci.Tech. Project(2014072704011254) ; Wuhan ChenGuang Youth Sci.Tech. Project(2014072704011254) ; National Basic Research Program (973 Program) of China(2013cb733205) ; National Basic Research Program (973 Program) of China(2013cb733205) ; National Basic Research Program (973 Program) of China(2013cb733205) ; National Basic Research Program (973 Program) of China(2013cb733205) ; National Key Technologies Research and Development Program of China(2016YFB0502604) ; National Key Technologies Research and Development Program of China(2016YFB0502604) ; National Key Technologies Research and Development Program of China(2016YFB0502604) ; National Key Technologies Research and Development Program of China(2016YFB0502604)
内容类型	期刊论文
源URL	[http://ir.hfcas.ac.cn:8080/handle/334002/30240]
专题	合肥物质科学研究院_应用技术研究所
作者单位	1.Chinese Acad Sci, Inst Geodesy & Geophys, Key Lab Monitoring & Estimate Environm & Disaster, Wuhan, Peoples R China 2.Chinese Acad Sci, Hefei Inst Phys Sci, Inst Technol Innovat, Hefei, Peoples R China
推荐引用方式 GB/T 7714	Li, Xiaodong,Du, Yun,Ling, Feng,et al. Locally adaptive linear mixture model-based super-resolution land-cover mapping based on a structure tensor[J]. INTERNATIONAL JOURNAL OF REMOTE SENSING,2016,37(24):5802-5825.
APA	Li, Xiaodong,Du, Yun,Ling, Feng,&Li, Wenbo.(2016).Locally adaptive linear mixture model-based super-resolution land-cover mapping based on a structure tensor.INTERNATIONAL JOURNAL OF REMOTE SENSING,37(24),5802-5825.
MLA	Li, Xiaodong,et al."Locally adaptive linear mixture model-based super-resolution land-cover mapping based on a structure tensor".INTERNATIONAL JOURNAL OF REMOTE SENSING 37.24(2016):5802-5825.