Use of MODIS and Landsat time series data to generate high-resolution temporal synthetic Landsat data using a spatial and temporal reflectance fusion model. J Appl Remote Sens, 2012, 6: 063507 修正的时空数据融合方法通过使用自适应窗口大小选择方法选择最佳窗口大小和移动步骤对粗大像素进行分解来改进STD...
spatio‐temporal data fusionRemote sensing data have been widely used to study various geophysical processes. With the advances in remote sensing technology, massive amount of remote sensing data are collected in space over time. Different satellite instruments typically have different footprints, ...
19. Roy, D.P.; Ju, J.; Lewis, P.; Schaaf, C.; Gao, F.; Hansen, M.; Lindquist, E. Multi-temporal MODIS-Landsat data fusion for relative radiometric normalization, gap filling, and prediction of Landsat data. Remote Sens. Environ. 2008, 112, 3112–3130. [CrossRef] 3. 分类法 在...
Spatio-temporal data fusion for very large remote sensing datasets. Technometrics 2014, 56, 174-185.Nguyen, H., Katzfuss, M., Cressie, N., and Braverman, A. (2014). "Spatio-temporal data fusion for very large remote sensing datasets". Technometrics, 56(2):174-185....
Remote sensing images and techniques are powerful tools to investigate earth’s surface. Data quality is the key to enhance remote sensing applications and obtaining clear and noise-free set of data is very difficult in most situations due to the varying
To obtain quality time series of satellite images in a human-dominated region, this study developed the Modified Flexible Spatial-temporal Data Fusion (MFSDAF) approach based on the Flexible Spatial-temporal Data Fusion (FSDAF) model by using the enhanced linear regression (ELR). Multiple ...
Furthermore, while daily data are sparse relative to the entire globe, the spatio-temporal domains are large and the AIRS dataset is massive. We describe a spatial-temporal data fusion methodology based on the Kalman filter and smoother that can combine complementary datasets to optimally estimate ...
Spatio‐Temporal data fusion for massive sea surface temperature data from MODIS and AMSR‐E instruments The land surface temperature (LST) is a key parameter for energy balance, evapotranspiration and climate change. In this study, two new methods of LST retr... P Ma,EL Kang - John Wiley ...
Another issue in multi-sensory data fusion is to eliminate spatiotemporal bias. Fusing data from multiple sensors suffers from time delays among measurement timestamps. Such delays might be caused by data transfer or signal processing. Therefore, data fusion applications are in need for accurate ...
A spatiotemporal statistical data fusion framework based on a Bayesian maximum entropy (BME) method was developed for merging satellite AOD products in East Asia. The advantages of the presented merging framework are that it not only utilizes the spatiotemporal autocorrelations but also explicitly ...