浏览全部资源
扫码关注微信
纸质出版日期： 2021-01-07 ，
扫 描 看 全 文
黄波，姜晓璐.2021.增强型空间像元分解时空遥感影像融合算法.遥感学报，25（1）： 241-250
Huang B and Jiang X L. 2021. An enhanced unmixing model for spatiotemporal image fusion. National Remote Sensing Bulletin， 25（1）：241-250
高空间、高时间分辨率的遥感影像对地表与大气环境的实时精细监测具有重要作用，但单一卫星传感器获取的遥感影像存在空间与时间分辨率相互制约的问题，时空融合技术发展成为了低成本、高效生成满足不同应用需求的高时空分辨率遥感影像的有效手段。近年来，国内外学者提出了大量的时空融合算法，但对于复杂的地物类型变化的空间细节修复仍存在挑战，融合影像精度有待提高。对此，本文提出增强型空间像元分解时空遥感影像融合算法（EUSTFM），采用变化检测识别并修复地物类型改变的像元，使空间像元分解过程可同时在已知时相与未知时相进行，以生成空间细节信息准确的中间分辨率影像对，用于最终的邻域相似像元计算，实现了对季节性变化（如植被自然生长）、有形变（如城市土地扩张）及无形变的地物类型变化（如农作物的成熟与收割）等复杂地表变化的一致性预测，提高了融合精度。实验采用两对Landsat-MODIS遥感影像数据集，对比STARFM与FSDAF两种广泛应用的时空融合算法，测试了该算法的影像融合效果。结果表明，本文提出的EUSTFM能够同时实现对季节性变化及复杂的地物类型变化的稳定预测，可生成具有更高精度的融合影像，将有效推动时空影像融合的实际遥感应用。
Remote sensing images with high spatial and temporal resolutions are vital for the real-time and fine monitoring of land surface and atmospheric environment. However
a single satellite sensor has to tradeoff between the spatial and temporal resolutions due to technical and budget limitations. In recent years
numerous spatial and temporal image fusion models have been proposed to produce high-resolution images with low cost and remarkable effectiveness. Despite the varying levels of success in the accuracy of fused images and the efficiency of algorithms
challenges always remain on the recovery of spatial details along with the complex land cover changes. This study presented an enhanced unmixing model for spatial and temporal image fusion (EUSTFM) that accounts for phenological changes (e.g.
vegetation growth) and shape (e.g.
urban expansion) and non-shape land cover changes (e.g.
crop rotation) on the land surface simultaneously. First
a change detection method was devised to identify the pixels with land cover change. The similar pixels of the detected pixels were then searched in the neighborhood to recompose the spectral reflectance on the prediction date. Thus
the real land cover class on the prediction date can be defined using the recomposed high-resolution image rather than directly using the classification result from a prior date. Subsequently
the spatial unmixing of pixels can be conducted on the prior and prediction dates to produce a medium-resolution image pair with accurate spatial details. Finally
the calculation of the similar pixels in the neighborhood was implemented for the final prediction of the fused images using all the original high and low-resolution image pair in the prior time
low-resolution image in the prediction time
and the produced medium-resolution image pair in the prior and prediction times. This study tested the algorithms with two actual Landsat-MODIS datasets: one dataset focusing on typical phenological changes in a complex landscape in Australia and the other dataset focusing on shape land cover changes in Shenzhen
China
to demonstrate the performance of the proposed EUSTFM for complex temporal changes on various landscapes. Comparisons with the popular spatiotemporal fusion models
including Spatial and Temporal Adaptive Reference Fusion Model (STARFM) and Flexible Spatiotemporal DAta Fusion (FSDAF)
showed that EUSTFM can robustly achieve a better fusion accuracy for all the phenological
non-shape
and shape land cover changes. The fused results using STARFM and FSDAF showed significant differences between the green band and the two other bands for typical phenological changes on a complex landscape in Australia. By contrast
the fused images using EUSTFM showed consistently high accuracy in all the three bands. This finding revealed a better performance for the fusion of images with various spatial resolution gaps
including a factor of 8 in near-infrared and red bands and a factor of 16 in the green bands. The proposed EUSTFM shows great potential in facilitating the monitoring of complex and diverse land surface dynamics.
时空融合遥感影像空间像元分解变化检测时间分辨率空间分辨率
spatial and temporal fusionremote sensing imagesspatial unmixing of pixelschange detectiontemporal resolutionspatial resolution
Busetto L, Meroni M and Colombo R. 2008. Combining medium and coarse spatial resolution satellite data to improve the estimation of sub-pixel NDVI time series. Remote Sensing of Environment, 112(1): 118-131 [DOI: 10.1016/j.rse.2007.04.004http://dx.doi.org/10.1016/j.rse.2007.04.004]
Chen X H, Li W T, Chen J, Rao Y H and Yamaguchi Y. 2014. A combination of TsHARP and thin plate spline interpolation for spatial sharpening of thermal imagery. Remote Sensing, 6(4): 2845-2863 [DOI: 10.3390/rs6042845http://dx.doi.org/10.3390/rs6042845]
Dowman I and Reuter H I. 2017. Global geospatial data from Earth observation: status and issues. International Journal of Digital Earth, 10(4): 328-341 [DOI: 10.1080/17538947.2016.1227379http://dx.doi.org/10.1080/17538947.2016.1227379]
Emelyanova I V, McVicar T R, Van Niel T G, Li L T and Van Dijk A I J M. 2013. Assessing the accuracy of blending Landsat-MODIS surface reflectances in two landscapes with contrasting spatial and temporal dynamics: a framework for algorithm selection. Remote Sensing of Environment, 133: 193-209 [DOI: 10.1016/j.rse.2013.02.007http://dx.doi.org/10.1016/j.rse.2013.02.007]
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]
Hilker T, Wulder M A, Coops N C, Linke J, McDermid G, Masek J G, Gao F and White J C. 2009. A new data fusion model for high spatial- and temporal-resolution mapping of forest disturbance based on Landsat and MODIS. Remote Sensing of Environment, 113(8): 1613-1627 [DOI: 10.1016/j.rse.2009.03.007http://dx.doi.org/10.1016/j.rse.2009.03.007]
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]
Huang B and Zhang H K. 2014. Spatio-temporal reflectance fusion via unmixing: accounting for both phenological and land-cover changes. International Journal of Remote Sensing, 35(16): 6213-6233 [DOI: 10.1080/01431161.2014.951097http://dx.doi.org/10.1080/01431161.2014.951097]
Huang B and Zhao Y Q. 2017. Research status and prospect of spatiotemporal fusion of multi-source satellite remote sensing imagery. Acta Geodaetica et Cartographica Sinica, 46(10): 1492-1499
黄波, 赵涌泉. 2017. 多源卫星遥感影像时空融合研究的现状及展望. 测绘学报, 46(10): 1492-1499 [DOI: 10.11947/J.AGCS.2017.20170376http://dx.doi.org/10.11947/J.AGCS.2017.20170376]
Jayalakshmi T and Santhakumaran A. 2011. Statistical normalization and back propagation for classification. International Journal of Computer Theory and Engineering, 3(1): 89-93 [DOI: 10.7763/IJCTE.2011.V3.288http://dx.doi.org/10.7763/IJCTE.2011.V3.288]
Ju J C and Roy D P. 2008. The availability of cloud-free Landsat ETM+ data over the conterminous United States and globally. Remote Sensing of Environment, 112(3): 1196-1211 [DOI: 10.1016/j.rse.2007.08.011http://dx.doi.org/10.1016/j.rse.2007.08.011]
Li S M, Sun D L, Goldberg M and Stefanidis A. 2013. Derivation of 30-m-resolution water maps from TERRA/MODIS and SRTM. Remote Sensing of Environment, 134: 417-430 [DOI: 10.1016/j.rse.2013.03.015http://dx.doi.org/10.1016/j.rse.2013.03.015]
Liu M, Yang W, Zhu X L, Chen J, Chen X H, Yang L Q and Helmer E H. 2019. An Improved Flexible Spatiotemporal DAta Fusion (IFSDAF) method for producing high spatiotemporal resolution normalized difference vegetation index time series. Remote Sensing of Environment, 227: 74-89 [DOI: 10.1016/j.rse.2019.03.012http://dx.doi.org/10.1016/j.rse.2019.03.012]
Lv Z Y, Liu T F, Zhang P L, Benediktsson J A, Lei T and Zhang X K. 2019. Novel adaptive histogram trend similarity approach for land cover change detection by using bitemporal very-high-resolution remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 57(12): 9554-9574 [DOI: 10.1109/TGRS.2019.2927659http://dx.doi.org/10.1109/TGRS.2019.2927659]
Ma Y, Chen F, Liu J B, He Y, Duan J B and Li X P. 2016. An automatic procedure for early disaster change mapping based on optical remote sensing. Remote Sensing, 8(4): 272 [DOI: 10.3390/rs804 0272http://dx.doi.org/10.3390/rs8040272]
Maselli F. 2001. Definition of spatially variable spectral endmembers by locally calibrated multivariate regression analyses. Remote Sensing of Environment, 75(1): 29-38 [DOI: 10.1016/S0034-4257(00)00153-Xhttp://dx.doi.org/10.1016/S0034-4257(00)00153-X]
Saah D, Tenneson K, Matin M, Uddin K, Cutter P, Poortinga A, Nguyen Q H, Patterson M, Johnson G, Markert K, Flores A, Anderson E, Weigel A, Ellenberg W L, Bhargava R, Aekakkararungroj A, Bhandari B, Khanal N, Housman I W, Potapov P, Tyukavina A, Maus P, Ganz D, Clinton N and Chishtie F. 2019. Land cover mapping in data scarce environments: challenges and opportunities. Frontiers in Environmental Science, 7: 150 [DOI: 10.3389/fenvs.2019.00150http://dx.doi.org/10.3389/fenvs.2019.00150]
Shi C L, Wang X H, Zhang M, Liang X J, Niu L Z, Han H Q and Zhu X M. 2019. A comprehensive and automated fusion method: the enhanced flexible spatiotemporal DAta fusion model for monitoring dynamic changes of land surface. Applied Sciences, 9(18): 3693 [DOI: 10.3390/app9183693http://dx.doi.org/10.3390/app9183693]
Song H H and Huang B. 2013. Spatiotemporal satellite image fusion through one-pair image learning. IEEE Transactions on Geoscience and Remote Sensing, 51(4): 1883-1896 [DOI: 10.1109/TGRS.2012.2213095http://dx.doi.org/10.1109/TGRS.2012.2213095]
Walker J, De Beurs K and Wynne R H. 2015. Phenological response of an Arizona dryland forest to short-term climatic extremes. Remote Sensing, 7(8): 10832-10855 [DOI: 10.3390/rs70810832http://dx.doi.org/10.3390/rs70810832]
Wang J and Huang B. 2017. A rigorously-weighted spatiotemporal fusion model with uncertainty analysis. Remote Sensing, 9(10): 990 [DOI: 10.3390/rs9100990http://dx.doi.org/10.3390/rs9100990]
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]
Wu M Q, Niu Z, Wang C Y, Wu C Y and Wang L. 2012. Use of MODIS and Landsat time series data to generate high-resolution temporal synthetic Landsat data using a spatial and temporal reflectance fusion model. Journal of Applied Remote Sensing, 6(1): 063507 [DOI: 10.1117/1.JRS.6.063507http://dx.doi.org/10.1117/1.JRS.6.063507]
Xie D F, Zhang J S, Sun P J, Pan Y Z, Yun Y and Yuan Z M Q. 2016. Remote sensing data fusion by combining STARFM and downscaling mixed pixel algorithm. Journal of Remote Sensing, 20(1): 62-72
谢登峰, 张锦水, 孙佩军, 潘耀忠, 云雅, 袁周米琪. 2016. 结合像元分解和STARFM模型的遥感数据融合. 遥感学报, 20(1): 62-72 [DOI: 10.11834/jrs.20165058http://dx.doi.org/10.11834/jrs.20165058]
Zhang B H, Zhang L, Xie D, Yin X L, Liu C J and Liu G. 2016. Application of synthetic NDVI time series blended from Landsat and MODIS data for grassland biomass estimation. Remote Sensing, 8(1): 10 [DOI: 10.3390/rs8010010http://dx.doi.org/10.3390/rs8010010]
Zhang M Z, Zhu D H, Su W, Huang J X, Zhang X D and Liu Z. 2019. Harmonizing multi-source remote sensing images for summer corn growth monitoring. Remote Sensing, 11(11): 1266 [DOI: 10.3390/rs11111266http://dx.doi.org/10.3390/rs11111266]
Zhao Y Q, Huang B and Song H H. 2018. A robust adaptive spatial and temporal image fusion model for complex land surface changes. Remote Sensing of Environment, 208: 42-62 [DOI: 10.1016/j.rse.2018.02.009http://dx.doi.org/10.1016/j.rse.2018.02.009]
Zhu X L, Cai F Y, Tian J Q and Williams T K A. 2018. Spatiotemporal fusion of multisource remote sensing data: literature survey, taxonomy, principles, applications, and future directions. Remote Sensing, 10(4): 527 [DOI: 10.3390/rs10040527http://dx.doi.org/10.3390/rs10040527]
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]
Zhukov B, Oertel D, Lanzl F and Reinhackel G. 1999. Unmixing-based multisensor multiresolution image fusion. IEEE Transactions on Geoscience and Remote Sensing, 37(3): 1212-1226 [DOI: 10.1109/36.763276http://dx.doi.org/10.1109/36.763276]
相关作者
相关机构
京公网安备11010802024621