Thermal satellite images are used to estimate Land Surface Temperature (LST), which aid in the monitoring of Urban Heat Islands (UHI). UHI studies are limited by the tradeoff in spatial and temporal resolution of satellite platforms. Epitomic representation have been proposed by Malkin et al. to create a high-resolution land label map by fusing a low-resolution land class map and high-resolution imagery. In this paper, we present an extension from classification to downscaling a continuous variable like LST. The approach is used to fuse 2km GOES-16’s LST with 30m NDVI and NDUSI from Landsat 8 to created 30m LST product.
KEYWORDS: Short wave infrared radiation, Data fusion, 3D modeling, Image fusion, Visualization, LIDAR, Data modeling, Image registration, Orthophoto maps, 3D image processing
We focus on the problem of spatial feature correspondence between images generated by sensors operating in different regions of the spectrum, in particular the Visible (Vis: 0.4-0.7 μm) and Shortwave Infrared (SWIR: 1.0-2.5 μm). Under the assumption that only one of the available datasets is geospatial ortho-rectified (e.g., Vis), this spatial correspondence can play a major role in enabling a machine to automatically register SWIR and Vis images, representing the same swath, as the first step toward achieving a full geospatial ortho-rectification of, in this case, the SWIR dataset. Assuming further that the Vis images are associated with a Lidar derived Digital Elevation Model (DEM), corresponding local spatial features between SWIR and Vis images can also lead to the association of all of the additional data available in these sets, to include SWIR hyperspectral and elevation data. Such a data association may also be interpreted as data fusion from these two sensing modalities: hyperspectral and Lidar. We show that, using the Scale Invariant Feature Transformation (SIFT) and Optimal Randomized RANdom Sample Consensus (RANSAC) algorithm, a software method can successfully find spatial correspondence between SWIR and Vis images for a complete pixel by pixel alignment. Our method is validated through an experiment using a large SWIR hyperspectral data cube, representing a portion of Los Angeles, California, and a DEM with associated Vis images covering a significantly wider area of Los Angeles.
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