面向非均质区域的空间增强型时空融合模型研究

doi:10.11947/j.AGCS.2023.20220519

摘要/Abstract

摘要： 随着遥感技术的发展,遥感数据日益增加。然而,受传感器限制及云雨天气影响,单一传感器难以获取高时空分辨率的遥感影像,从而在一定程度上影响全球及区域环境变化研究。遥感影像时空融合理论与技术的发展为解决这一问题提供了有效途径。近年来,国内外学者提出了大量的时空融合算法,但对于复杂地表景观区域空间细节的融合仍存在挑战,地表非均质区域时空融合的精度有待提高。为此,本文提出了一种面向非均质区域的空间增强型时空融合模型。首先,基于混合像元分解原理与遥感数据空间特征尺度不变性假设,将低分辨率光谱变化降尺度为高分辨率光谱变化值;然后,基于不同分辨率遥感数据光谱关系的时间不变性假设,获得最终融合影像。试验结果表明,相对于常用融合模型STARFM、FSDAF,本文模型既能有效反映不同地物物候变化信息,同时能更好地保留地表的空间细节,增强了非均质地表覆盖区域融合影像的空间特征与效果;本文模型的均方根误差RMSE、相关系数r及结构相似性指标SSIM平均值分别达到0.024、0.898、0.897,相对于常用融合模型STARFM、FSDAF,RMSE平均值分别降低了6.71%和4.33%,r平均值分别提高了1.95%和1.74%,SSIM平均值分别提高了2.33%和2.08%。本文模型精度高,模型简单、易于操作,尤其在非均质地表覆盖区域能够取得良好的融合精度与效果,具有良好的应用前景。

关键词: 时空融合, 多源遥感, 空间增强, 子像元, 空间异质性

Abstract: With the development of remote sensing technology, remote sensing data has been increased rapidly. However, due to the limitation of sensors and the influence of cloud and rain weather, it is difficult for a single sensor to obtain remote sensing images with high spatial-temporal resolution, which affects the study of global and regional environmental change to a certain extent. The development of spatio-temporal fusion theory and technology of remote sensing image provides an effective way to solve this problem. In recent years, a number of spatio-temporal fusion algorithms have been proposed. However, there are still challenges to spatio-temporal fusion for accuracy and spatial detail of heterogeneity areas. Therefore, this paper proposes a spatially enhanced spatio-temporal fusion model for heterogeneity regions. Firstly, based on the principle of spectral mixing analysis and the assumption of spatial characteristics invariance of remote sensing data, the low-resolution spectral changes are downscaled to high-resolution. Secondly, based on the assumption of spectral invariance relationship of remote sensing data with different resolutions, the final fusion image is obtained. The experimental results show that compared with the commonly used STARFM and FSDAF models, the spatially enhanced spatio-temporal fusion model for heterogeneity regions can not only predict the phenological change information of different ground features effectively, but also preserves the spatial details of the ground surface and enhances the spatial characteristics and fusion effect in heterogeneous surface area; The mean values of root mean square error (RMSE), correlation coefficient (r) and structural similarity index (SSIM) of the spatially enhanced spatio-temporal fusion model reached 0.024, 0.898 and 0.897, respectively. Compared with the commonly used STARF and FSDAF models, the mean value of RMSE decreased by 6.71% and 4.33%, respectively; the average value of r increased by 1.95% and 1.74%, respectively; the average value of SSIM increased by 2.33% and 2.08%, respectively. The proposed spatially enhanced spatio-temporal fusion model for heterogeneity regions has the advantages of high fusion accuracy, simple and easy operation, especially in the heterogeneous surface coverage area. Therefore, the spatially enhanced spatio-temporal fusion model for heterogeneity regions has good prospects in remote sensing applications.

Key words: spatio-temporal fusion, multi-source remote sensing, spatial enhancement, sub-pixel, spatial heterogeneity

中图分类号:

P237

皮新宇, 曾永年, 王盼成. 面向非均质区域的空间增强型时空融合模型研究[J]. 测绘学报, 2023, 52(10): 1714-1723.

PI Xinyu, ZENG Yongnian, WANG Pancheng. Spatially enhanced spatio-temporal fusion model for heterogeneity regions[J]. Acta Geodaetica et Cartographica Sinica, 2023, 52(10): 1714-1723.

参考文献

[1] DOWMAN I, REUTER H I. Global geospatial data from earth observation:status and issues[J]. International Journal of Digital Earth, 2017, 10(4):328-341.
[2] 宫鹏.遥感科学与技术中的一些前沿问题[J].遥感学报, 2009, 13(1):13-23. GONG Peng. Some frontier problems in remote sensing science and technology[J]. Journal of Remote Sensing, 2009, 13(1):13-23.
[3] 龚健雅,钟燕飞.光学遥感影像智能化处理研究进展[J].遥感学报, 2016, 20(5):733-747. GONG Jianya, ZHONG Yanfei. Survey of intelligent optical remote sensing image processing[J]. Journal of Remote Sensing, 2016, 20(5):733-747.
[4] 李德仁,童庆禧,李荣兴,等.高分辨率对地观测的若干前沿科学问题[J].中国科学:地球科学, 2012, 42(6):805-813. LI Deren, TONG Qingxi, LI Rongxing, et al. Some frontier scientific problems of high-resolution earth observation[J]. Scientia Sinica (Terrae), 2012, 42(6):805-813.
[5] 童庆禧,张兵,张立福.中国高光谱遥感的前沿进展[J].遥感学报, 2016, 20(5):689-707. TONG Qingxi, ZHANG Bing, ZHANG Lifu. Current progress of hyperspectral remote sensing in China[J]. Journal of Remote Sensing, 2016, 20(5):689-707.
[6] WOODCOCK C E, LOVELAND T R, HEROLD M, et al. Transitioning from change detection to monitoring with remote sensing:a paradigm shift[J]. Remote Sensing of Environment, 2020, 238:111558.
[7] CHEN Bin, HUANG Bo, XU Bing. Comparison of spatiotemporal fusion models:a review[J]. Remote Sensing, 2015, 7(2):1798-1835.
[8] 黄波,赵涌泉.多源卫星遥感影像时空融合研究的现状及展望[J].测绘学报, 2017, 46(10):1492-1499. DOI:10.11947/j.AGCS.2017. 20170376. HUANG Bo, ZHAO Yongquan. Research status and prospect of spatiotemporal fusion of multi-source satellite remote sensing imagery[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(10):1492-1499. DOI:10.11947/j.AGCS.2017. 20170376.
[9] 刘建波,马勇,武易天,等.遥感高时空融合方法的研究进展及应用现状[J].遥感学报, 2016, 20(5):1038-1049. LIU Jianbo, MA Yong, WU Yitian, et al. Review of methods and applications of high spatiotemporal fusion of remote sensing data[J]. Journal of Remote Sensing, 2016, 20(5):1038-1049.
[10] BELGIU M, STEIN A. Spatiotemporal image fusion in remote sensing[J]. Remote Sensing, 2019, 11(7):818.
[11] ZHANG H K, HUANG B, ZHANG M, et al. A generalization of spatial and temporal fusion methods for remotely sensed surface parameters[J]. International Journal of Remote Sensing, 2015, 36(17):4411-4445.
[12] ZHU Xiaolin, CAI Fangyi, TIAN Jiaqi, et al. Spatiotemporal fusion of multisource remote sensing data:literature survey, taxonomy, principles, applications, and future directions[J]. Remote Sensing, 2018, 10(4):527.
[13] 张良培,何江,杨倩倩,等.数据驱动的多源遥感信息融合研究进展[J].测绘学报, 2022, 51(7):1317-1337. DOI:10.11947/j.AGCS.2022. 20220171. ZHANG Liangpei, HE Jiang, YANG Qianqian, et al. Data-driven multi-source remote sensing data fusion:progress and challenges[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(7):1317-1337. DOI:10.11947/j.AGCS.2022. 20220171.
[14] ZHAO Yi, JIANG Mi, MA Zhangfeng. Integration of SAR polarimetric features and multi-spectral data for object-based land cover classification[J]. Journal of Geodesy and Geoinformation Science, 2019, 2(4):64-72.
[15] ZHUKOV B, OERTEL D, LANZL F, et al. Unmixing-based multisensor multiresolution image fusion[J]. IEEE Transactions on Geoscience and Remote Sensing, 1999, 37(3):1212-1226.
[16] LIU Wenjie, ZENG Yongnian, LI Songnian, et al. Spectral unmixing based spatiotemporal downscaling fusion approach[J]. International Journal of Applied Earth Observation and Geoinformation, 2020, 88:102054.
[17] CAI Jiajun, HUANG Bo, FUNG T. Progressive spatiotemporal image fusion with deep neural networks[J]. International Journal of Applied Earth Observation and Geoinformation, 2022, 108:102745.
[18] GAO Feng, MASEK J, SCHWALLER M, et al. On the blending of the Landsat and MODIS surface reflectance:predicting daily Landsat surface reflectance[J]. IEEE Transactions on Geoscience and Remote Sensing, 2006, 44(8):2207-2218.
[19] GAO Feng, ANDERSON M C, KUSTAS W P, et al. Simple method for retrieving leaf area index from Landsat using MODIS leaf area index products as reference[J]. Journal of Applied Remote Sensing, 2012, 6(1):063554.
[20] HILKER T, WULDER M A, COOPS N C, et al. Generation of dense time series synthetic Landsat data through data blending with MODIS using a spatial and temporal adaptive reflectance fusion model[J]. Remote Sensing of Environment, 2009, 113(9):1988-1999.
[21] 柳文杰,曾永年,张猛.融合时间序列环境卫星数据与物候特征的水稻种植区提取[J].遥感学报, 2018, 22(3):381-391. LIU Wenjie, ZENG Yongnian, ZHANG Meng. Mapping rice paddy distribution by using time series HJ blend data and phenological parameters[J]. Journal of Remote Sensing, 2018, 22(3):381-391.
[22] WATTS J D, POWELL S L, LAWRENCE R L, et al. Improved classification of conservation tillage adoption using high temporal and synthetic satellite imagery[J]. Remote Sensing of Environment, 2011, 115(1):66-75.
[23] WENG Qihao, FU Peng, GAO Feng. Generating daily land surface temperature at Landsat resolution by fusing Landsat and MODIS data[J]. Remote Sensing of Environment, 2014, 145:55-67.
[24] ZHANG Hankui, CHEN J M, HUANG Bo, et al. Reconstructing seasonal variation of landsat vegetation index related to leaf area index by fusing with MODIS data[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(3):950-960.
[25] 张猛,曾永年.融合高时空分辨率数据估算植被净初级生产力[J].遥感学报, 2018, 22(1):143-152. ZHANG Meng, ZENG Yongnian. Net primary production estimation by using fusion remote sensing data with high spatial and temporal resolution[J]. Journal of Remote Sensing, 2018, 22(1):143-152.
[26] ZHANG Meng, ZENG Yongnian, HUANG Wei, et al. Combining spatiotemporal fusion and object-based image analysis for improving wetland mapping in complex and heterogeneous urban landscapes[J]. Geocarto International, 2019, 34(10):1144-1161.
[27] 皮新宇,曾永年,贺城墙.融合多源遥感数据的高分辨率城市植被覆盖度估算[J].遥感学报, 2021, 25(6):1216-1226. PI Xinyu, ZENG Yongnian, HE Chengqiang. High-resolution urban vegetation coverage estimation based on multi-source remote sensing data fusion[J]. National Remote Sensing Bulletin, 2021, 25(6):1216-1226.
[28] HILKER T, WULDER M A, COOPS N C, et al. A new data fusion model for high spatial-and temporal-resolution mapping of forest disturbance based on Landsat and MODIS[J]. Remote Sensing of Environment, 2009, 113(8):1613-1627.
[29] GEVAERT C M, GARCÍA-HARO F J. A comparison of STARFM and an unmixing-based algorithm for Landsat and MODIS data fusion[J]. Remote Sensing of Environment, 2015, 156:34-44.
[30] ROY D P, JU Junchang, LEWIS P, et al. Multi-temporal MODIS-Landsat data fusion for relative radiometric normalization, gap filling, and prediction of Landsat data[J]. Remote Sensing of Environment, 2008, 112(6):3112-3130.
[31] ZHU Xiaolin, CHEN Jin, GAO Feng, et al. An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions[J]. Remote Sensing of Environment, 2010, 114(11):2610-2623.
[32] ZHU Xiaolin, HELMER E H, GAO Feng, et al. A flexible spatiotemporal method for fusing satellite images with different resolutions[J]. Remote Sensing of Environment, 2016, 172:165-177.
[33] LIU Wenjie, ZENG Yongnian, LI Songnian, et al. An improved spatiotemporal fusion approach based on multiple endmember spectral mixture analysis[J]. Sensors, 2019, 19(11):2443.
[34] GUO Dizhou, SHI Wenzhong, HAO Ming, et al. FSDAF 2. 0:improving the performance of retrieving land cover changes and preserving spatial details[J]. Remote Sensing of Environment, 2020, 248:111973.
[35] 黄波,姜晓璐.增强型空间像元分解时空遥感影像融合算法[J].遥感学报, 2021, 25(1):241-250. HUANG Bo, JIANG Xiaolu. An enhanced unmixing model for spatiotemporal image fusion[J]. National Remote Sensing Bulletin, 2021, 25(1):241-250.
[36] LI Xiaodong, FOODY G M, BOYD D S, et al. SFSDAF:an enhanced FSDAF that incorporates sub-pixel class fraction change information for spatio-temporal image fusion[J]. Remote Sensing of Environment, 2020, 237:111537.
[37] HOU Shuwei, SUN Wenfang, GUO Baolong, et al. Adaptive-SFSDAF for spatiotemporal image fusion that selectively uses class abundance change information[J]. Remote Sensing, 2020, 12(23):3979