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ABSTRACT:Linear Imaging Self Scanning Sensor (LISS-3) onboard Resoucesat-1 and 2 Satellites have been used extensively for various land cover-land use applications. In this study, we examined the potential of using Resourcesat-2 LISS-3 images in the absence of LANDSAT-8 Operational Land Imager (OLI) images. This paper compares the capabilities of LISS-3 sensor with OLI sensor. LISS-3 images were selected for comparison because of their close resemblance in electromagnetic spectrum range with LS-8 OLI images. Images of LS-8 OLI and RS-2 LISS-3 of the same area in Andhra Pradesh were used to evaluate the comparative performances based on the intra-inter band correlation, spectral vegetation indices and land cover classification. The results showed that in most cases the LS-8 OLI and the RS-2 LISS-3 images are comparable. This study also indicated that LISS-3 images could fill the data gaps in OLI images for land-cover studies, vice versa.

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Abstract-In this work, we propose and evaluate different scene based methods for non-uniformity corrections for optical remote sensing data sets. These methods can be used to correct or refine the existing radiometric calibrations, thereby improving the image quality. The performance of each algorithm against different datasets are analyzed and a quantitative comparison of different quality parameters viz. entropy, correlation coefficient, signal to noise ratio, peak signal to noise ratio and structural similarity index are carried out to recommend the best method for each scene. For a given data set, the selected method depends on the severity, type of terrain it covered, etc.

Abstract-The quality of remote sensing satellite images are expressed in terms of ground sample distance, modular transfer function, signal to noise ratio and National Imagery Interpretability Rating Scale (NIIRS) by user community. The proposed system estimates NIIRS of an image, by incorporating a new automated method to calculate the Relative Edge Response (RER). The prominent edges which contribute the most for the estimation of RER are uniquely extracted with a combined application of certain filters and morphological operators. RER is calculated from both horizontal and vertical edges separately and the geometric mean is considered as the final result. Later applying the estimated RER along with other parameters, the system returns the NIIRS value of the input image. This work has proved the possible implementation of automated techniques to estimate the NIIRS from images and specifics in the metafile contents of imagery.