In order to interpret the data captured by the MTI, clouds must be removed from the images using a cloud mask. These algorithms are created by determining the important characteristics of clouds and determining thresholds for these attributes. The main characteristics of clouds are that they are white, bright, and cold. A cloud mask algorithm was created for the MTI using thresholds for these characteristics. Data to create the mask was collected from a visible band, a near infrared band, and a thermal band. Once data was collected and the thresholds were determined, an algorithm was created which would determine if clouds were present in images taken by the MTI.
This cloud mask was compared with two other existing cloud making algorithms, GENIE and MODUS. The MODerate resolution Imaging Spectroradiometer (MODIS) cloud mask requires additional information to run: is it day or night, is it a sun glint region, does the image contain snow or ice, ect. This method works well in regions containing snow or ice, but takes significant computing power to run. The GENIE method uses a genetic algorithm to create tools for processing images. GENIE takes several hours to evolve a solution, but then applies the cloud mask quickly. In comparison, the threshold system created for the MTI requires thresholds to be entered. The algorithm then applies the mask, which gives accurate results relatively quickly.
A cloud mask approach to MTI images is viable and practical option for environmental characterization, however a different approach was needed for the nonproliferation aspects of the mission.
In operational remote sensing, the implicit model for cloud geometry is a homogeneous plane-parallel slab of infinite horizontal extent. Each pixel is indeed processed as if it exchanged no radiant energy whatsoever with its neighbors. The shortcomings of this conceptual model have been well documented in the specialized literature but rarely mitigated. The worst-case scenario is probably high-resolution imagery where dense isolated clouds are visible, often both bright (reflective) and dark (transmissive) sides being apparent from the same satellite viewing angle: the low transmitted radiance could conceivably be interpreted in plane-parallel theory as no cloud at all. An alternative to the plane-parallel cloud model is introduced here that has the same appeal of being analytically tractable, at least in the diffusion limit: the spherical cloud. This new geometrical paradigm is applied to radiances from cumulus clouds captured by DOE’s Multispectral Thermal Imager (MTI). Estimates of isolated cloud opacities are a necessary first step in correcting radiances from surface targets that are visible in the midst of a broken-cloud field. This type of advanced atmospheric correction is badly needed in remote sensing applications such as nonproliferation detection were waiting for a cloud-free look in the indefinite future is not a viable option.