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Spatio-temporal fusion quality evaluation based on “Point”-“Line”-“Plane” aspects

  • role： First author第一作者
  • Affiliation：

    Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, China

  • Introduction：雷晨阳，1995年生，女，硕士研究生，研究方向遥感数据时—空融合的研究应用。E-mail: sketchlcy@126.com
LEI Chenyang1,  
  • role： Corresponding author通信作者
  • Affiliation：

    Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, China

    College of Electrical and Information Engineering, Hunan University, Changsha 410082, China

  • Email：mengxiangchao@nbu.edu.cn
  • Introduction：孟祥超，1989年生，男，讲师，研究方向为多源遥感数据融合与应用。E-mail: mengxiangchao@nbu.edu.cn
MENG Xiangchao12*,  
  • Affiliation：

    Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, China

SHAO Feng1

résumé

Remote sensing images with both high spatial and high temporal resolutions are highly desirable in various applications. However, due to technical limitations of remote sensing imaging system and other factors, the acquired remote sensing images have to make a fundamental trade-off between high spatial and temporal resolutions. For example, the MODIS images have high temporal resolution, its spatial resolution is low; On the contrary, the Landsat images have high spatial resolution with relatively lower temporal resolution.Spatio-temporal fusion can integrate the complementary advantages of high spatial resolution and high spectral resolution, respectively, of multi-source remote sensing images, to generate time-continuous images with high spatial resolution. This has important application value in remote sensing image dynamic monitoring, time-series analysis, and other aspects. To the best of our knowledge, at present, most of studies generally evaluated the spatio-temporal fused images based on a single type of remote sensing data, such as the surface reflectance data or the Normalized Difference Vegetation Index (NDVI) remote sensing product, etc. However, how well a spatio-temporal fusion method performs in practical applications? This should be comprehensively assessed from different aspects based on different types of remote sensing data products. In addition, most of studies performed the evaluation of a spatio-temporal fusion method based on the fused image at a single phase. However, for spatio-temporal fusion, the final target is actually to obtain time-series fused images, the quality evaluation of the fused images from the temporal dimension should be also taken into account. Whereas, to the best of our knowledge, the quality evaluation for time-series fused images is not comprehensively considered in the existing studies. In this paper, we proposed to evaluate the spatio-temporal fusion methods from the comprehensive perspective of single time point, time series, and multiple different remote sensing data products. In this paper, spatio-temporal fusion data sets, including surface reflectance data set, NDVI data set, and the Land Surface Temperature (LST) data set were established based on Landsat and MODIS remote sensing satellite images. In addition, some typical spatio-temporal fusion methods were reviewed, and the performance of four spatio-temporal fusion algorithms, including the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), Enhanced STARFM (ESTARFM), Flexible Spatio-Temporal Data Fusion (FSDAF), and the Spatial and Temporal Nonlocal Filter-based Fusion Model (STNLFFM), were qualitatively and quantitatively evaluated based on the proposed data sets of different kinds of remote sensing data products, i.e., the surface reflectance, NDVI, and LST. In addition, the quality evaluation from the perspective of both single-time-point and time-series dimensions were performed. The experimental results show that the performance of spatio-temporal fusion algorithms can be more comprehensively verified based on different type of data sets, and the evaluation combined with single time point and time-series data set is more objective.

mots-clés

spatio-temporal fusion;reflectance;Normalized Difference Vegetation Index(NDVI);Land Surface Temperature(LST);quality evaluation

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