Review of methods and applications of high spatiotemporal fusion of remote sensing data

	Review of methods and applications of high spatiotemporal fusion of remote sensing data
	Liu, Jianbo 1; Ma, Yong 1; Wu, Yitian 1; Chen, Fu 1
刊名	Yaogan Xuebao/Journal of Remote Sensing
	2016
卷号	20 期号:5 页码:1038-1049
通讯作者	Ma, Yong (mayong@radi.ac.cn)
英文摘要	Remote sensing images can provide important and abundant information about the Earth at a global or local scale. Thus, many applications often require remote sensing data with high acquisition frequency and high spatial resolution. However, meeting this requirement is a considerable challenge given satellite limitations. The spatiotemporal fusion method provides a feasible way to solve these "spatialtemporal" contradictions. In the last 10 years, spatiotemporal fusion has elicited wide interest in various applications because it integrates the superiority of multisource satellite data in fine spatial resolution or frequent temporal coverage and it can generate fused images with high spatial and temporal resolution. In this study, we reviewed the advantages and limitations of three types of method for spatiotemporal fusion, namely, transformation- based, reconstruction-based, and learning-based methods. First, the transformation-based method consistently filters and processes transformed data and then accesses high-spatiotemporal resolution data via inverse transform. It mainly focuses on the spatial and spectral information of multi-source satellite image enhancement or fusion. The spatial resolution of the results obtained with this method remains low, and the accuracy is relatively poor because the temporal change information is not used in this method. Second, the reconstruction-based method has elicited much attention since the proposal of a semi-physical fusion model and STARFM. This method integrates the information of temporal change, spatial change, and spectral change among multi-source satellite images acquired in different times and generates high-spatiotemporal resolution data by calculating the weight of different changes. This method provides an excellent fusion approach for spatiotemporal fusion because the results show high accuracy. However, the results would be poor when the type of land cover changes or the cover area is heterogeneous. Third, the learning-based method is based on the development of compressed sensing and sparse representation technology. This method represents a recent development that relies on learning the relationship and difference of multi-source satellite images by training samples and constructing an image dictionary. Although the learning-based method could obtain good results, the processing efficiency is lower than that of other methods, and it requires the training of sample selection. Recently, the result of spatiotemporal fusion has been used in various applications, especially in the reconstruction-based method. This method is mainly used in time series data analysis as well as in retrieval and regional data set generation. For time series data analysis and retrieval, many researchers have used the results in developing the missing images of time series, detecting phenology, inversing urban environment parameters, estimating gross primary production, evaluating biomass, calculating land surface temperature, and so on. Given that the covered area of a low spatial resolution is large and the spectrum continuity of spatiotemporal fusion results is high, these results could be applied to the generation of regional data sets. Although the spatiotemporal fusion method has seen considerable development, certain problems remain. The uncertainties are attributed to the complexity of land cover change, the errors of sensor calibration, and the data pretreatment process. The five potential aspects of the spatiotemporal fusion method that require further study are the consistency of data from different sensors, introduction of nonlinear mixed models, addition of prior knowledge, introduction of deep learning theory, and expansion into other satellites. © 2016, Science Press. All right reserved.
收录类别	EI
语种	中文
WOS记录号	WOS:20164402965595
内容类型	期刊论文
源URL	[http://ir.radi.ac.cn/handle/183411/39594]
专题	遥感与数字地球研究所_SCI/EI期刊论文_期刊论文
作者单位	1. Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 2.100094, China 3. University of Chinese Academy of Sciences, Beijing 4.100049, China
推荐引用方式 GB/T 7714	Liu, Jianbo,Ma, Yong,Wu, Yitian,et al. Review of methods and applications of high spatiotemporal fusion of remote sensing data[J]. Yaogan Xuebao/Journal of Remote Sensing,2016,20(5):1038-1049.
APA	Liu, Jianbo,Ma, Yong,Wu, Yitian,&Chen, Fu.(2016).Review of methods and applications of high spatiotemporal fusion of remote sensing data.Yaogan Xuebao/Journal of Remote Sensing,20(5),1038-1049.
MLA	Liu, Jianbo,et al."Review of methods and applications of high spatiotemporal fusion of remote sensing data".Yaogan Xuebao/Journal of Remote Sensing 20.5(2016):1038-1049.