A Spectrophotometric Evaluation of Lunar Catharina Crater Using Support Vector Regression Analysis for FeO and TiO<sub>2</sub> Estimations
Support Vector Regression (SVR) is an extended version of the Support Vector Machine (SVM) algorithm. It is an effective machine learning tool for handling huge complex data sets. SVR algorithm is introduced to the existing lunar FeO and TiO<sub>2</sub> concentrate estimation techniques....
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2025-07-01
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| Series: | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://isprs-annals.copernicus.org/articles/X-G-2025/607/2025/isprs-annals-X-G-2025-607-2025.pdf |
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| author | A. K. Padinharethodi A. K. Padinharethodi S. Kumar Advaith C A |
| author_facet | A. K. Padinharethodi A. K. Padinharethodi S. Kumar Advaith C A |
| author_sort | A. K. Padinharethodi |
| collection | DOAJ |
| description | Support Vector Regression (SVR) is an extended version of the Support Vector Machine (SVM) algorithm. It is an effective machine learning tool for handling huge complex data sets. SVR algorithm is introduced to the existing lunar FeO and TiO<sub>2</sub> concentrate estimation techniques. This machine learning algorithm is capable of transforming complex nonlinear problems into a higher dimensional feature space and solving it linearly. The SVR analysis of Moon Mineralogy Mapper (M3) data for lunar mineral concentrate estimation shows an upgraded result over the existing estimation methods. Outlier points are less sensitive to SVR and, hence it provides the best fit line or curve. |
| format | Article |
| id | doaj-art-d8e0591a933b4fbf95a2b0ebd410bde8 |
| institution | OA Journals |
| issn | 2194-9042 2194-9050 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj-art-d8e0591a933b4fbf95a2b0ebd410bde82025-08-20T02:36:35ZengCopernicus PublicationsISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences2194-90422194-90502025-07-01X-G-202560761210.5194/isprs-annals-X-G-2025-607-2025A Spectrophotometric Evaluation of Lunar Catharina Crater Using Support Vector Regression Analysis for FeO and TiO<sub>2</sub> EstimationsA. K. Padinharethodi0A. K. Padinharethodi1S. Kumar2Advaith C A3Al Sobaki General Maintenance Co LLC, Al Ain, UAEPhotogrammetry & Remote Sensing Department, Remote Sensing and Geoinformatics Group, Indian Institute of Remote Sensing, Indian Space Research Organisation, Dehradun, IndiaPhotogrammetry & Remote Sensing Department, Remote Sensing and Geoinformatics Group, Indian Institute of Remote Sensing, Indian Space Research Organisation, Dehradun, IndiaPhotogrammetry & Remote Sensing Department, Remote Sensing and Geoinformatics Group, Indian Institute of Remote Sensing, Indian Space Research Organisation, Dehradun, IndiaSupport Vector Regression (SVR) is an extended version of the Support Vector Machine (SVM) algorithm. It is an effective machine learning tool for handling huge complex data sets. SVR algorithm is introduced to the existing lunar FeO and TiO<sub>2</sub> concentrate estimation techniques. This machine learning algorithm is capable of transforming complex nonlinear problems into a higher dimensional feature space and solving it linearly. The SVR analysis of Moon Mineralogy Mapper (M3) data for lunar mineral concentrate estimation shows an upgraded result over the existing estimation methods. Outlier points are less sensitive to SVR and, hence it provides the best fit line or curve.https://isprs-annals.copernicus.org/articles/X-G-2025/607/2025/isprs-annals-X-G-2025-607-2025.pdf |
| spellingShingle | A. K. Padinharethodi A. K. Padinharethodi S. Kumar Advaith C A A Spectrophotometric Evaluation of Lunar Catharina Crater Using Support Vector Regression Analysis for FeO and TiO<sub>2</sub> Estimations ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | A Spectrophotometric Evaluation of Lunar Catharina Crater Using Support Vector Regression Analysis for FeO and TiO<sub>2</sub> Estimations |
| title_full | A Spectrophotometric Evaluation of Lunar Catharina Crater Using Support Vector Regression Analysis for FeO and TiO<sub>2</sub> Estimations |
| title_fullStr | A Spectrophotometric Evaluation of Lunar Catharina Crater Using Support Vector Regression Analysis for FeO and TiO<sub>2</sub> Estimations |
| title_full_unstemmed | A Spectrophotometric Evaluation of Lunar Catharina Crater Using Support Vector Regression Analysis for FeO and TiO<sub>2</sub> Estimations |
| title_short | A Spectrophotometric Evaluation of Lunar Catharina Crater Using Support Vector Regression Analysis for FeO and TiO<sub>2</sub> Estimations |
| title_sort | spectrophotometric evaluation of lunar catharina crater using support vector regression analysis for feo and tio sub 2 sub estimations |
| url | https://isprs-annals.copernicus.org/articles/X-G-2025/607/2025/isprs-annals-X-G-2025-607-2025.pdf |
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