Abstract
Image acquired through various sensors accrue multi-faceted distortions due to the failure of either sensor or platform and consequently, images get distorted. But for any kind of image analysis, it is a prerequisite that each image pixel should be refurbished. In recompensing these, image processing assists in image restoring to its best possible natural form. Recent image processing techniques have significantly advanced and are capable of removing any kind distortions. The present work exhibits the statistical image processing approach, which has been tested over the Landsat series of satellite image having data gaps of approximately 22% of the loss from the normal scene area that occurred due to the failure of Scan Line Corrector (SLC). The method has precisely estimated the missing values to fill the data gaps in the images for making more visually sensible and analytical. The results presented and authenticated the statistical processing approach as a potential tool for gap filling of lost pixels for the satellite imagery, which can enable more scientific usage of the acquired data sets.
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Acknowledgements
We gratefully acknowledge USGS and NASA leadership and support of the Landsat Science Team. Landsat ETM+ data used in this study was downloaded from http://earthexplorer.usgs.gov/. Source for this data set is the Global Land Cover Facility, www.landcover.org. This work was undertaken as part of full-time PhD program and the work was supported by the DST-INSPIRE Fellowship [Grant Numbers IF120639] from Ministry of Science & Technology, Govt. of India for completing PhD work. This research is supported in part by SERB under Early Career Research Scheme (FILE NO. ECR/2017/000816).
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Kumar, D. (2020). Statistical Image Processing for Enhanced Scientific Analysis. In: Somani, A.K., Shekhawat, R.S., Mundra, A., Srivastava, S., Verma, V.K. (eds) Smart Systems and IoT: Innovations in Computing. Smart Innovation, Systems and Technologies, vol 141. Springer, Singapore. https://doi.org/10.1007/978-981-13-8406-6_1
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DOI: https://doi.org/10.1007/978-981-13-8406-6_1
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