Stray light in an imaging system can be unpredictable and may not originate directly from the field of view of the imaged pixel. Depending on the severity of the stray light, this can adversely affect the quality of remote sensing imagery and be difficult to correct. A significant amount of stray light can be visually noticeable and even distracting to the image analyst. Complicating the matter, stray light that originates from outside the field of view can be highly unpredictable and variable across the image, adversely affecting the results of image processing algorithms developed for automated processing. A new algorithm has been developed in an attempt to estimate the amount of stray light in each remote sensing image. The stray light algorithm estimates an average and maximum amount of stray light for each image, and can output an estimated stray light map. The algorithm can be run in an automated batch processing mode for operational monitoring where the results are placed into a database along with other image quality trending factors. This paper presents results of testing the algorithm on simulated and commercial panchromatic and multispectral imagery.
For multispectral imagery (MSI), spatial registration between bands is a very important part of the overall quality of the MSI product. For some remote sensing imagery, mis-registration of bands greater than about one-quarter of a pixel can be visually noticeable. Based on the successful registration processing developed for the Multispectral Thermal Imager (MTI) (known as edgereg), a derivative algorithm was developed for assessment of spatial registration accuracy of 4-band MSI. This algorithm has sub-pixel accuracy of a few hundredths of a pixel, and can be used to compute an image average mis-registration or process imagery in a block mode to create a mis-registration map. This paper presents the results of testing the algorithm on simulated and commercial imagery.
KEYWORDS: Clouds, Thermal modeling, Data modeling, Humidity, Solar radiation models, Thermal effects, Solar radiation, Shortwaves, Atmospheric modeling, Temperature metrology
Total sky irradiance onto the Earth's surface includes contributions from solar (or shortwave) radiation as well as thermal (longwave) radiation. Whereas shortwave downwelling is only present during daylight hours, thermal downwelling radiation is present throughout the day and night. Sky thermal irradiance on the Earth's surface has been described in other references as a function of surface ambient temperature and relative humidity. In this study, we show that with the introduction of low overcast clouds (altitude less than 2km and 100% cloud cover), thermal downwelling sky irradiance increases 34%. A comprehensive model was developed to compute the thermal downwelling sky irradiance as a function of temperature, relative humidity, cloud height, and percent cloud cover. Based on ground truth measurements collected in Reston, Virginia, we propose coefficients to model the total thermal downwelling irradiance including cloud effects with an operational error of 9.7%.
High resolution panchromatic imagery can be used to increase the spatial resolution of low resolution spectral imagery through spatial/spectral sharpening techniques. Recently, sharpening techniques have been presented that use a multiresolution analysis by manipulating the images at different resolution scales that use a Laplacian pyramid or wavelet transform. This paper presents a model for sharpening multispectral images (MRA/MTF) that uses multiresolution analysis (MRA) to extract the high frequency information from the panchromatic image and matches the spatial response between imagery using a modulation transfer function (MTF) correction. When carefully executed, the MRA/MTF model is shown to provide a sharpened image with minimum spectral distortion and visually pleasing results. Multispectral data was used to evaluate the algorithm for sharpening 30 meter Landsat data with 1 meter aerial photography with comparison to other sharpening algorithms available within ERDAS Imagine and with ERIM's sharpening algorithm called Sparkle. The algorithms were tested for spectral distortion by comparing the covariance between bands before and after sharpening.
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