遥感数据时空融合研究进展及展望

doi:10.6046/gtzyyg.2018.02.01

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遥感数据在空间分辨率和时间分辨率上相互制约,单一的卫星传感器不能获得既具有高空间分辨率又具有高时间分辨率的数据,遥感数据时空融合技术是目前解决此问题的重要方法之一。对此介绍了国内外在遥感数据时空融合领域的主要研究成果,通过对当前主流融合模型构建理论进行对比分析,将时空融合模型分为基于变换的模型和基于像元重构的模型,并且进一步将基于像元重构的模型分为了基于线性混合模型和时空自适应融合模型2类,分别介绍了各类模型的基本原理与方法,并对模型的优缺点进行了对比分析。最后,对时空融合模型的发展趋势从数据、应用和尺度3个方面进行了展望。

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	蒙继华

关键词 ：遥感数据, 时空融合, 模型, 对比分析, 展望

Abstract：

Taking the interaction between spatial and temporal resolution of remote sensing data into consideration, the authors hold that there is no satellite sensor that can produce images with both high spatial and temporal resolution, and spatiotemporal fusion of remote sensing data is an effective method to solve this problem. This paper introduces main research achievements of spatiotemporal fusion model obtained both in China and abroad. Based on the comparative analysis of the mainstream fusion models, these models can be divided into two categories, i.e., the transformation-based model and the pixel-reconstruction-based model. Furthermore, the authors divide the pixel-reconstruction-based model into mixed linear model and spatial and temporal adaptive reflectance model, and then introduce the basic principles, methods of these models. This paper makes a comparative analysis of the advantages and disadvantages of various aspects of the model. At last, the data, application and scale prospect of spatiotemporal fusion models are put forward.

Key words： remote sensing data spatiotemporal fusion model comparison prospect

收稿日期: 2016-10-18 出版日期: 2018-05-30

TP751.1

基金资助:国家自然科学基金面上项目“基于HJ-1数据的作物成熟期遥感预测方法研究”(编号: 4117133141171331);国家高技术研究发展计划“863”计划课题“典型应用领域全球定量遥感产品生产体系”(编号: 2013AA12A302);中国科学院科技服务网络计划(STS)项目“精准农业技术体系研发及先进设备完善和升级”(编号: KFJ-EW-STS-069)

通讯作者: 蒙继华

引用本文:

董文全, 蒙继华. 遥感数据时空融合研究进展及展望[J]. 国土资源遥感, 2018, 30(2): 1-11.
Wenquan DONG, Jihua MENG. Review of spatiotemporal fusion model of remote sensing data. Remote Sensing for Land & Resources, 2018, 30(2): 1-11.

链接本文:

https://www.gtzyyg.com/CN/10.6046/gtzyyg.2018.02.01 或 https://www.gtzyyg.com/CN/Y2018/V30/I2/1

算法类型	二级分类	名称	参考文献	至少所需中高空间分辨率数据个数/期	实验所用数据	适用尺度	异质性较强区域适用性	算法特点
基于变换的模型	基于小波变换的模型	小波变换	顾晓鹤等^[9]	1	MODIS归一化植被指数(normalized difference vegetation index,NDVI), TM NDVI	中、大尺度	较差	所用MODIS NDVI数据为16 d产品,物候差异特征不够明显; 融合数据存在混合像元问题
		小波变换	Acerbi-Junior等^[10]	1	MODIS, TM	中、大尺度	较差	有效地提高了MODIS数据的空间分辨率,为最小失真情况下提高源图像的空间分辨率提供了一个概念框架
		小波变换	Wu等^[11]	1	MODIS, TM	中、大尺度	较差	评价了小波变换在时空融合中的潜力,研究发现选择合适的小波函数和融合方法是小波变换的关键
	基于主成分分析的模型	主成分分析	Shevyrnogov等^[12]	1	NOAA NDVI, MSS	中、大尺度	较差	通过融合MSS亮度分量和NOAA NDVI数据得到高时空分辨率NDVI数据
基于像元重构的模型	基于线性混合模型	线性回归和决策树	Hansen等^[13]	1	MODIS, ETM+	尤其大尺度	差	适用于地物单一且反射率呈线性变换的区域,大大减少时空融合所需时间,算法可移植
		线性回归	Zhukov等^[14]	1	AVHRR, TM	中、大尺度	差	考虑了像元反射率空间可变性的问题,引入窗口技术,为后续研究所采用
		线性回归	Maselli^[15]	1	AVHRR NDVI, TM NDVI	中、大尺度	较差	提出了距离权重的概念,即认为距离目标像元越近,对目标像元的影响越大
		线性回归	Busetto等^[16]	1	MODIS, TM	中、大尺度	较好	提出了光谱权重的概念,主要解决线性混合模型解算过程中像元反射率的空间可变性问题
	基于时空自适应融合模型	时空自适应性反射率融合模型	Gao等^[17]	1	MODIS, ETM+	中、小尺度	较好	不仅考虑与目标像元的空间距离和光谱相似性,还考虑了时间上的差异,并且利用邻近光谱相似像元计算中心像元,大大提高了结果精度
		针对反射率变化的时空自适应融合模型	Hilker等^[18]	2	MODIS, TM,ETM+	中、小尺度	好	能够捕获比较短暂的地表变化
		改进型时空自适应融合模型	Zhu等^[19]	2	MODIS, TM	中、小尺度	好	根据空间和光谱相似性来估计中心像元,适用于异质性较强的非植被覆盖地区
		不同时空分辨率NDVI的时空融合模型	蒙继华等^[20,21]	1	MODIS, TM,HJ-1 CCD	中、小尺度	好	考虑了物候的影响,直接将算法用于植被指数

Tab.1 时空融合模型汇总

Fig.1 多源遥感数据时空融合

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