This paper deals with a calibration algorithm to be used with data from the Thermal Infrared Sensor (TIRS) on board Landsat 8. Some non-uniform banding calibration errors have been observed in TIRS data since it was launched in 2013. Investigations have shown that this artifact is due to out-of-field radiance that scatters onto the TIRS focal plane. A calibration algorithm which utilizes TIRS image data itself to correct the stray light error has been proposed and implemented. Preliminary experiments have indicated this methodology reduces stray light artifacts significantly. However, there are some cases in which the TIRS TIRS method may not optimally mitigate stray light. These are special cases where there is a large temperature contrast between the edge of the TIRS image and of out-of-field radiance. This paper outlines an alternative approach with near-coincident image data from an external satellite sensor and compares the correction results with the current operational method, in general, and for some of the out-of-field special cases.
In this paper, we present an improved Edge Directed Super Resolution (EDSR) technique to produce enhanced edge
definition and improved image quality in the resulting high resolution image. The basic premise behind this algorithm
remains, like its predecessor, to utilize gradient and spatial information and interpolate along the edge direction in a
multiple pass iterative fashion. The edge direction map generated from horizontal and vertical gradients and resized to
the target resolution is quantized into eight directions over a 5 × 5 block compared to four directions over a 3 × 3 block in
the previous algorithm. This helps reduce the noise caused in part due to the quantization error and the super resolved
results are significantly improved. In addition, an appropriate weighting encompassing the degree of similarity between
the quantized edge direction and the actual edge direction is also introduced. In an attempt to determine the optimal super
resolution parameters for the case of still image capture, a hardware setup was utilized to investigate and evaluate those
factors. In particular, the number of images captured as well as the amount of sub pixel displacement that yield a high
quality result was studied. This is done by utilizing a XY stage capable of sub-pixel movement. Finally, an edge
preserving smoothing algorithm contributes to improved results by reducing the high frequency noise introduced by the
super resolution process. The algorithm showed favorable results on a wide variety of datasets obtained from
transportation to multimedia based print/scan application in addition to images captured with the aforementioned
hardware setup.
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