气候变化领域集成服务门户(CCPortal): Superresolution Mapping of Remotely Sensed Image Based on Hopfield Neural Network With Anisotropic Spatial Dependence Model

CCPortal

DOI	10.1109/LGRS.2013.2291778
	Superresolution Mapping of Remotely Sensed Image Based on Hopfield Neural Network With Anisotropic Spatial Dependence Model
	Li, Xiaodong; Du, Yun; Ling, Feng; Feng, Qi; Fu, Bitao
发表日期	2014
ISSN	1545-598X
EISSN	1558-0571
卷号	11 期号:7
英文摘要	Superresolution mapping (SRM) based on the Hopfield neural network (HNN) is a technique that produces land cover maps with a finer spatial resolution than the input land cover fraction images. In HNN-based SRM, it is assumed that the spatial dependence of land cover classes is homogeneous. HNN-based SRM uses an isotropic spatial dependence model and gives equal weights to neighboring subpixels in the neighborhood system. However, the spatial dependence directions of different land cover classes are discarded. In this letter, a revised HNN-based SRM with anisotropic spatial dependence model (HNNA) is proposed. The Sobel operator is applied to detect the gradient magnitude and direction of each fraction image at each coarse-resolution pixel. The gradient direction is used to determine the direction of subpixel spatial dependence. The gradient magnitude is used to determine the weights of neighboring subpixels in the neighborhood system. The HNNA was examined on synthetic images with artificial shapes, a synthetic IKONOS image, and a real Landsat multispectral image. Results showed that the HNNA can generate more accurate superresolution maps than a traditional HNN model.
关键词	Anisotropic spatial dependence model Hopfield neural network (HNN)sobel operator superresolution mapping (SRM)
学科领域	Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology
语种	英语
WOS研究方向	Geochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
来源期刊	IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
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/111748
作者单位	Chinese Acad Sci, Inst Geodesy & Geophys, Key Lab Monitoring & Estimate Environm & Disaster, Wuhan 430077, Peoples R China
推荐引用方式 GB/T 7714	Li, Xiaodong,Du, Yun,Ling, Feng,et al. Superresolution Mapping of Remotely Sensed Image Based on Hopfield Neural Network With Anisotropic Spatial Dependence Model[J]. 中国科学院西北生态环境资源研究院,2014,11(7).
APA	Li, Xiaodong,Du, Yun,Ling, Feng,Feng, Qi,&Fu, Bitao.(2014).Superresolution Mapping of Remotely Sensed Image Based on Hopfield Neural Network With Anisotropic Spatial Dependence Model.IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,11(7).
MLA	Li, Xiaodong,et al."Superresolution Mapping of Remotely Sensed Image Based on Hopfield Neural Network With Anisotropic Spatial Dependence Model".IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 11.7(2014).