Abstract
ISRO’s Imaging InfraRed Spectrometer (IIRS) onboard Chandrayaan-2 is relatively the most advanced spectrometer in the lunar orbit till date. IIRS operates from 0.8 to 5µm spectral range and has been sampling the lunar surface in 250 spectral channels with 20 nm spectral and 80 m spatial resolution. The data are transmitted in strips of hyper-spectral cubes. These datasets do not have spatial information embedded in them. Spatial information is provided as a separate geometry file containing line/sample wise lat/long information. In order to impart spatial information to cubes, multiple steps are involved requiring manual intervention. This approach is time consuming as well as prone to error. Since most tools available are proprietary, it is not possible to acquire and understand the source code and supplement it to run on multiple datasets automatically. In order to overcome these challenges SelenoRef, a python-based tool has been developed to automate the process of Seleno-referencing of Ch-2 IIRS hyper-spectral cubes. SelenoRef requires three inputs viz. input folder containing downloaded zipped files, output folder for processed data and desired projection system. SelenoRef autonomously unzips all datasets from the input folder and navigates through the directory structure to locate hyper-spectral cubes, geometry information file and metadata file to retrieve the dataset, lat/long information and projection, respectively. Cubes are seleno-referenced using Ground Control Points (GCPs), band statistics are calculated and output is generated in the output directory. SelenoRef is open source and has a graphical user interface.
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Department of Science and Technology, Government of India, IF200115, Ishan Rayal
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Rayal, I., Chauhan, P., Thakur, P.K. et al. Python-Based Open-Source Tool for Automating Seleno-Referencing of Chandrayaan-2 Hyper-Spectral Data Cubes. J Indian Soc Remote Sens 52, 305–313 (2024). https://doi.org/10.1007/s12524-024-01814-4
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DOI: https://doi.org/10.1007/s12524-024-01814-4