浏览全部资源
扫码关注微信
Published： 07 March 2021 ，
扫 描 看 全 文
雷晨阳，孟祥超，邵枫.2021.遥感影像时—空融合的“点”—“线”—“面”质量评价.遥感学报，25（3）： 791-802
Lei C Y，Meng X C and Shao F. 2021. Spatio-temporal fusion quality evaluation based on “Point”-“Line”-“Plane” aspects. National Remote Sensing Bulletin， 25（3）：791-802
遥感影像时—空融合可集成多源数据高空间分辨率和高时间分辨率互补优势，生成时间连续的高空间分辨率影像，在遥感影像的动态监测与时序分析等方面具有重要应用价值。然而，现有多数研究往往基于单一数据产品对时—空融合算法进行评价，而在实际生产应用中，需要验证算法在多种遥感产品数据的融合表现；此外，目前研究大多基于“单点时刻”进行评价，忽略了时—空融合在“时间线”上的有效验证。本文提出遥感影像时—空融合的“点”—“线”—“面”多角度综合质量评价策略，基于Landsat TM和MODIS影像，建立了时—空融合系列数据集，包括地表反射率、植被指数和地表温度，并在此基础上从单时相（“点”）、时间序列（“线”）、多种数据产品（“面”）多个角度对4种典型融合算法进行定性和定量的综合评价。结果表明，基于不同产品类型的数据集更能充分验证算法性能，且结合单点时刻和时间序列的评价更加客观。
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.
时—空融合地表反射率植被指数地表温度质量评价
spatio-temporal fusionreflectanceNormalized Difference Vegetation Index(NDVI)Land Surface Temperature(LST)quality evaluation
Ball G H and Hall D J. 1965. ISODATA, A Novel Method of Data Analysis and Pattern Classification. Menlo Park: Stanford Research Institute
Buades A, Coll B and Morel J M. 2005a. A non-local algorithm for image denoising//Proceedings of 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Diego: IEEE [DOI: 10.1109/CVPR.2005.38http://dx.doi.org/10.1109/CVPR.2005.38]
Buades A, Coll B and Morel J M. 2005b. A review of image denoising algorithms, with a new one. Multiscale Modeling and Simulation, 4(2): 490-530 [DOI: 10.1137/040616024http://dx.doi.org/10.1137/040616024]
Chen B, Huang B and Xu B. 2015. Comparison of spatiotemporal fusion models: a review. Remote Sensing, 7(2): 1798-1835 [DOI: 10.3390/rs70201798http://dx.doi.org/10.3390/rs70201798]
Cheng Q, Liu H Q, Shen H F, Wu P H and Zhang L P. 2017. A spatial and temporal nonlocal filter-based data fusion method. IEEE Transactions on Geoscience and Remote Sensing, 55(8): 4476-4488 [DOI: 10.1109/tgrs.2017.2692802http://dx.doi.org/10.1109/tgrs.2017.2692802]
Dennison P E, Halligan K Q and Roberts D A. 2004. A comparison of error metrics and constraints for multiple endmember spectral mixture analysis and spectral angle mapper. Remote Sensing of Environment, 93(3): 359-367 [DOI: 10.1016/j.rse.2004.07.013http://dx.doi.org/10.1016/j.rse.2004.07.013]
Dubrule O. 1984. Comparing splines and kriging. Computers and Geosciences, 10(2/3): 327-338 [DOI: 10.1016/0098-3004(84)90030-xhttp://dx.doi.org/10.1016/0098-3004(84)90030-x]
Gao F, Masek J, Schwaller M and Hall F. 2006. On the blending of the Landsat and MODIS surface reflectance: predicting daily Landsat surface reflectance. IEEE Transactions on Geoscience and Remote Sensing, 44(8): 2207-2218 [DOI: 10.1109/tgrs.2006.872081http://dx.doi.org/10.1109/tgrs.2006.872081]
Gevaert C M and García-Haro F J. 2015. A comparison of STARFM and an unmixing-based algorithm for Landsat and MODIS data fusion. Remote Sensing of Environment, 156: 34-44 [DOI: 10.1016/j.rse.2014.09.012http://dx.doi.org/10.1016/j.rse.2014.09.012]
Hazaymeh K and Almagbile A. 2018. A comparative analysis of spatiotemporal data fusion models for Landsat and modis data. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-3: 491-495 [DOI: 10.5194/isprs-archives-XLII-3-491-2018http://dx.doi.org/10.5194/isprs-archives-XLII-3-491-2018]
Huang B and Song H H. 2012. Spatiotemporal reflectance fusion via sparse representation. IEEE Transactions on Geoscience and Remote Sensing, 50(10): 3707-3716 [DOI: 10.1109/TGRS.2012.2186638http://dx.doi.org/10.1109/TGRS.2012.2186638]
Khan M M, Alparone L and Chanussot J. 2009. Pansharpening quality assessment using the modulation transfer functions of instruments. IEEE Transactions on Geoscience and Remote Sensing, 47(11): 3880-3891 [DOI: 10.1109/tgrs.2009.2029094http://dx.doi.org/10.1109/tgrs.2009.2029094]
Li L, Wang X Q and Li M M. 2011. Study on the fusion of MODIS and TM images using the spectral response Function and STARFM algorithm//Proceedings of 2011 International Conference on Image Analysis and Signal Processing. Hubei: IEEE: 171-176 [DOI: 10.1109/IASP.2011.6109023http://dx.doi.org/10.1109/IASP.2011.6109023]
Maselli F, Chiesi M and Pieri M. 2019. A new method to enhance the spatial features of multitemporal NDVI image series. IEEE Transactions on Geoscience and Remote Sensing, 57(7): 4967-4979 [DOI: 10.1109/tgrs.2019.2894850http://dx.doi.org/10.1109/tgrs.2019.2894850]
Meng X C, Sun W W, Ren K, Yang G, Fu R D. 2020. Spatial-spectral fusion of GF-5/GF-1 remote sensing images based on multiresolution analysis. Journal of Remote Sensing. 24(04):379-387
孟祥超,孙伟伟,任凯,杨刚,邵枫,符冉迪. 2020. 基于多分辨率分析的GF-5和GF-1遥感影像空—谱融合.遥感学报, 24(04):379-387[DOI: 1007-4619（2020）04-0379-09http://dx.doi.org/1007-4619（2020）04-0379-09]
Qin Z, Karnieli A and Berliner P. 2001. A mono-window algorithm for retrieving land surface temperature from Landsat TM data and its application to the Israel-Egypt border region. International Journal of Remote Sensing, 22(18): 3719-3746 [DOI: 10.1080/01431160010006971http://dx.doi.org/10.1080/01431160010006971]
Quan J L. 2017. Blending multi-spatiotemporal resolution land surface temperatures over heterogeneous surfaces//Proceedings of 2017 Joint Urban Remote Sensing Event. Dubai: IEEE [DOI: 10.1109/JURSE.2017.7924596http://dx.doi.org/10.1109/JURSE.2017.7924596]
Shen H F, Meng X C and Zhang L P. 2016. An integrated framework for the spatio-temporal-spectral fusion of remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 54(12): 7135-7148 [DOI: 10.1109/TGRS.2016.2596290http://dx.doi.org/10.1109/TGRS.2016.2596290]
Su X, Deledalle C A, Tupin F and Sun H. 2012. Two steps multi-temporal non-local means for SAR images//Proceedings of 2012 IEEE International Geoscience and Remote Sensing Symposium. Munich: IEEE [DOI: 10.1109/IGARSS.2012.6351106http://dx.doi.org/10.1109/IGARSS.2012.6351106]
Wang Z, Bovik A C, Sheikh H R and Simoncelli E P. 2004. Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4): 600-612 [DOI: 10.1109/TIP.2003.819861http://dx.doi.org/10.1109/TIP.2003.819861]
Yang M, Yang G J, Chen X N, Zhang Y F and You J N. 2018. Generation of land surface temperature with high spatial and temporal resolution based on FSDAF method. Remote Sensing for Land and Resources, 30(1): 54-62
杨敏, 杨贵军, 陈晓宁, 张勇峰, 尤静妮. 2018. 基于FSDAF方法融合生成高时空分辨率地表温度. 国土资源遥感, 30(1): 54-62 [DOI: 10.6046/gtzyyg.2017.04.34http://dx.doi.org/10.6046/gtzyyg.2017.04.34]
Zhang H K, Huang B, Zhang M, Cao K and Yu L. 2015. A generalization of spatial and temporal fusion methods for remotely sensed surface parameters. International Journal of Remote Sensing, 36(17): 4411-4445 [DOI: 10.1080/01431161.2015.1083633http://dx.doi.org/10.1080/01431161.2015.1083633]
Zhu X L, Chen J, Gao F, Chen X H and Masek J G. 2010. An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions. Remote Sensing of Environment, 114(11): 2610-2623 [DOI: 10.1016/j.rse.2010.05.032http://dx.doi.org/10.1016/j.rse.2010.05.032]
Zhu X L, Helmer E H, Gao F, Liu D S, Chen J and Lefsky M A. 2016. A flexible spatiotemporal method for fusing satellite images with different resolutions. Remote Sensing of Environment, 172: 165-177 [DOI: 10.1016/j.rse.2015.11.016http://dx.doi.org/10.1016/j.rse.2015.11.016]
相关作者
相关机构
京公网安备11010802024621