Technical Note
Version 1
Preserved in Portico This version is not peer-reviewed
An Algorithm for High-Resolution Satellite Imagery Pre-processing
Version 1
: Received: 4 March 2022 / Approved: 7 March 2022 / Online: 7 March 2022 (09:43:08 CET)
How to cite: Shrawankar, U.; Shrawankar, C. An Algorithm for High-Resolution Satellite Imagery Pre-processing. Preprints 2022, 2022030095. https://doi.org/10.20944/preprints202203.0095.v1 Shrawankar, U.; Shrawankar, C. An Algorithm for High-Resolution Satellite Imagery Pre-processing. Preprints 2022, 2022030095. https://doi.org/10.20944/preprints202203.0095.v1
Abstract
During the few years, various algorithms have been developed to extract features from high-resolution satellite imagery. For the classification of these extracted features, several complex algorithms have been developed. But these algorithms do not possess critical refining stages of processing the data at the preliminary phase. Various satellite sensors have been launched such as LISS3, IKONOS, QUICKBIRD, and WORLDVIEW etc. Before classification and extraction of semantic data, imagery of the high resolution must be refined. The whole refinement process involves several steps of interaction with the data. These steps are pre-processing algorithms that are presented in this paper. Pre-processing steps involves Geometric correction, radiometric correction, Noise removal, Image enhancement etc. Due to these pre-processing algorithms, the accuracy of the data is increased. Various applications of these pre-processing of the data are in meteorology, hydrology, soil science, forest, physical planning etc. This paper also provides a brief description of the local maximum likelihood method, fuzzy method, stretch method and pre-processing methods, which are used before classifying and extracting features from the image.
Keywords
pre-processing; image transformation; image enhancement; geometric correction; radiometric correction; Satellite Imagery
Subject
Engineering, Control and Systems Engineering
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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