A Bayesian Data Fusion Approach to Spatio-Temporal Fusion of Remotely Sensed Images

被引:97
|
作者
Xue, Jie [1 ]
Leung, Yee [1 ,2 ,3 ]
Fung, Tung [1 ,2 ,4 ]
机构
[1] Chinese Univ Hong Kong, Dept Geog & Resource Management, Hong Kong, Hong Kong, Peoples R China
[2] Chinese Univ Hong Kong, Inst Future Cities, Hong Kong, Hong Kong, Peoples R China
[3] Chinese Univ Hong Kong, Big Data Decis Analyt Ctr, Hong Kong, Hong Kong, Peoples R China
[4] Chinese Univ Hong Kong, Inst Environm Energy & Sustainabil, Hong Kong, Hong Kong, Peoples R China
关键词
Bayesian data fusion; Landsat; MODIS; spatio-temporal image fusion; time series; MODIS SURFACE REFLECTANCE; TIME-SERIES; LANDSAT DATA; RESOLUTION ENHANCEMENT; MULTITEMPORAL MODIS; TEMPORAL DATA; MODEL; SPARSE; GENERATION; ALGORITHM;
D O I
10.3390/rs9121310
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Remote sensing provides rich sources of data for the monitoring of land surface dynamics. However, single-sensor systems are constrained from providing spatially high-resolution images with high revisit frequency due to the inherent sensor design limitation. To obtain images high in both spatial and temporal resolutions, a number of image fusion algorithms, such as spatial and temporal adaptive reflectance fusion model (STARFM) and enhanced STARFM (ESTARFM), have been recently developed. To capitalize on information available in a fusion process, we propose a Bayesian data fusion approach that incorporates the temporal correlation information in the image time series and casts the fusion problem as an estimation problem in which the fused image is obtained by the Maximum A Posterior (MAP) estimator. The proposed approach provides a formal framework for the fusion of remotely sensed images with a rigorous statistical basis; it imposes no requirements on the number of input image pairs; and it is suitable for heterogeneous landscapes. The approach is empirically tested with both simulated and real-life acquired Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) images. Experimental results demonstrate that the proposed method outperforms STARFM and ESTARFM, especially for heterogeneous landscapes. It produces surface reflectances highly correlated with those of the reference Landsat images. It gives spatio-temporal fusion of remotely sensed images a solid theoretical and empirical foundation that may be extended to solve more complicated image fusion problems.
引用
收藏
页数:23
相关论文
共 50 条
  • [1] A NEW SPATIO-TEMPORAL FUSION METHOD FOR REMOTELY SENSED DATA BASED ON CONVOLUTIONAL NEURAL NETWORKS
    Li, Yunfei
    Liu, Chenying
    Yan, Lin
    Li, Jun
    Plaza, Antonio
    Li, Bo
    [J]. 2019 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2019), 2019, : 835 - 838
  • [2] A multispectral spatio-temporal approach for cloud screening of remotely sensed images
    Addesso, Paolo
    Conte, Roberto
    Longo, Maurizio
    Restaino, Rocco
    Vivone, Gemine
    [J]. REMOTE SENSING OF CLOUDS AND THE ATMOSPHERE XVI, 2011, 8177
  • [3] Video modeling by spatio-temporal resampling and Bayesian fusion
    Zheng, Yunfei
    Li, Xin
    [J]. 2007 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, VOLS 1-7, 2007, : 3201 - 3204
  • [4] SPATIO-TEMPORAL TOF DATA ENHANCEMENT BY FUSION
    Garcia, Frederic
    Aouada, Djamila
    Mirbach, Bruno
    Ottersten, Bjoern
    [J]. 2012 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP 2012), 2012, : 981 - 984
  • [5] Spatio-Temporal Fusion: A Fusion Approach for Point Cloud Sparsity Problem
    Zhao, Chongjun
    Xu, Haoran
    Xu, Hua
    Lai, Kexue
    Cen, Ming
    [J]. 2023 35TH CHINESE CONTROL AND DECISION CONFERENCE, CCDC, 2023, : 4836 - 4841
  • [6] Spatio-Temporal Data Fusion for Satellite Images Using Hopfield Neural Network
    Fung, Che Heng
    Wong, Man Sing
    Chan, P. W.
    [J]. REMOTE SENSING, 2019, 11 (18)
  • [7] A model-based approach to multiresolution fusion in remotely sensed images
    Joshi, Manjunath V.
    Bruzzone, Lorenzo
    Chaudhuri, Subhasis
    [J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2006, 44 (09): : 2549 - 2562
  • [8] Remotely sensed images and GIS data fusion for automatic change detection
    Li, Deren
    [J]. INTERNATIONAL JOURNAL OF IMAGE AND DATA FUSION, 2010, 1 (01) : 99 - 108
  • [9] Data Fusion of Satellite Remotely Sensed Images and its Application in Agriculture
    Zhuang Xiao-yun
    Shi Run-he
    Liu Chao-shun
    [J]. PIAGENG 2010: PHOTONICS AND IMAGING FOR AGRICULTURAL ENGINEERING, 2010, 7752
  • [10] Probabilistic fusion of spatio-temporal data to estimate stream flow via Bayesian networks
    Nagarajan, K.
    Krekeler, C.
    Slatton, K. C.
    [J]. IGARSS: 2007 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, VOLS 1-12: SENSING AND UNDERSTANDING OUR PLANET, 2007, : 4870 - 4873