气候变化领域集成服务门户(CCPortal): A spatial-temporal Hopfield neural network approach for super-resolution land cover mapping with multi-temporal different resolution remotely sensed images

CCPortal

DOI	10.1016/j.isprsjprs.2014.03.013
	A spatial-temporal Hopfield neural network approach for super-resolution land cover mapping with multi-temporal different resolution remotely sensed images
	Li, Xiaodong; Ling, Feng; Du, Yun; Feng, Qi; Zhang, Yihang
发表日期	2014
ISSN	0924-2716
EISSN	1872-8235
卷号	93
英文摘要	The mixed pixel problem affects the extraction of land cover information from remotely sensed images. Super-resolution mapping (SRM) can produce land cover maps with a finer spatial resolution than the remotely sensed images, and reduce the mixed pixel problem to some extent. Traditional SRMs solely adopt a single coarse-resolution image as input. Uncertainty always exists in resultant fine-resolution land cover maps, due to the lack of information about detailed land cover spatial patterns. The development of remote sensing technology has enabled the storage of a great amount of fine spatial resolution remotely sensed images. These data can provide fine-resolution land cover spatial information and are promising in reducing the SRM uncertainty. This paper presents a spatial-temporal Hopfield neural network (STHNN) based SRM, by employing both a current coarse-resolution image and a previous fine-resolution land cover map as input. STHNN considers the spatial information, as well as the temporal information of sub-pixel pairs by distinguishing the unchanged, decreased and increased land cover fractions in each coarse-resolution pixel, and uses different rules in labeling these sub-pixels. The proposed STHNN method was tested using synthetic images with different class fraction errors and real Landsat images, by comparing with pixel-based classification method and several popular SRM methods including pixel-swapping algorithm, Hopfield neural network based method and sub-pixel land cover change mapping method. Results show that STHNN outperforms pixel-based classification method, pixel-swapping algorithm and Hopfield neural network based model in most cases. The weight parameters of different STHNN spatial constraints, temporal constraints and fraction constraint have important functions in the STHNN performance. The heterogeneity degree of the previous map and the fraction images errors affect the STHNN accuracy, and can be served as guidances of selecting the optimal STHNN weight parameters. (C) 2014 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) Published by Elsevier B.V. All rights reserved.
关键词	Land cover Mapping Change detection Multitemporal Super-resolution mapping Hopfield neural network
学科领域	Physical Geography; Geology; Remote Sensing; Imaging Science & Photographic Technology
语种	英语
WOS研究方向	Geography, Physical ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
来源期刊	ISPRS JOURNAL OF PHOTOGRAMMETRY AND 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/111746
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,Ling, Feng,Du, Yun,et al. A spatial-temporal Hopfield neural network approach for super-resolution land cover mapping with multi-temporal different resolution remotely sensed images[J]. 中国科学院西北生态环境资源研究院,2014,93.
APA	Li, Xiaodong,Ling, Feng,Du, Yun,Feng, Qi,&Zhang, Yihang.(2014).A spatial-temporal Hopfield neural network approach for super-resolution land cover mapping with multi-temporal different resolution remotely sensed images.ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING,93.
MLA	Li, Xiaodong,et al."A spatial-temporal Hopfield neural network approach for super-resolution land cover mapping with multi-temporal different resolution remotely sensed images".ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 93(2014).