Assessing Landsat Processing Levels and Support Vector Machine Classification
The availability of different processing levels for satellite images makes it important to measure their suitability for classification tasks. This study investigates the impact of the Landsat data processing level on the accuracy of land cover classification using a support vector machine (SVM) cl...
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University of Baghdad
2025-01-01
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| Series: | Ibn Al-Haitham Journal for Pure and Applied Sciences |
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| Online Access: | https://jih.uobaghdad.edu.iq/index.php/j/article/view/3992 |
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| author | Nehad Al-Salmany Taghreed A. Naji |
| author_facet | Nehad Al-Salmany Taghreed A. Naji |
| author_sort | Nehad Al-Salmany |
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The availability of different processing levels for satellite images makes it important to measure their suitability for classification tasks. This study investigates the impact of the Landsat data processing level on the accuracy of land cover classification using a support vector machine (SVM) classifier. The classification accuracy values of Landsat 8 (LS8) and Landsat 9 (LS9) data at different processing levels vary notably. For LS9, Collection 2 Level 2 (C2L2) achieved the highest accuracy of (86.55%) with the polynomial kernel of the SVM classifier, surpassing the Fast Line-of-Sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) at (85.31%) and Collection 2 Level 1 (C2L1) at (84.93%). The LS8 data exhibits similar behavior. Conversely, when using the maximum-likelihood classifier, the highest accuracy (83.06%) was achieved with FLAASH. The results demonstrate significant variations in accuracies for different land cover classes, which emphasizes the importance of per-class accuracy. The results highlight the critical role of preprocessing techniques and classifier selection in optimizing the classification processes and land cover mapping accuracy for remote sensing geospatial applications. Finally, the actual differences in classification accuracy between processing levels are larger than those given by the confusion matrix. So, the consideration of alternative evaluation methods with the absence of reference images is critical.
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| format | Article |
| id | doaj-art-239c27cda3f34e13929d85437f9ecb92 |
| institution | Kabale University |
| issn | 1609-4042 2521-3407 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | University of Baghdad |
| record_format | Article |
| series | Ibn Al-Haitham Journal for Pure and Applied Sciences |
| spelling | doaj-art-239c27cda3f34e13929d85437f9ecb922025-01-22T01:21:03ZengUniversity of BaghdadIbn Al-Haitham Journal for Pure and Applied Sciences1609-40422521-34072025-01-0138110.30526/38.1.3992Assessing Landsat Processing Levels and Support Vector Machine ClassificationNehad Al-Salmany0https://orcid.org/0009-0005-9604-3324Taghreed A. Naji 1https://orcid.org/0000-0002-5893-6608Department of Physics, College of Education for Pure Science (Ibn-Al-Haitham), University of Baghdad, Baghdad, Iraq.Department of Physics, College of Education for Pure Science (Ibn-Al-Haitham), University of Baghdad, Baghdad, Iraq. The availability of different processing levels for satellite images makes it important to measure their suitability for classification tasks. This study investigates the impact of the Landsat data processing level on the accuracy of land cover classification using a support vector machine (SVM) classifier. The classification accuracy values of Landsat 8 (LS8) and Landsat 9 (LS9) data at different processing levels vary notably. For LS9, Collection 2 Level 2 (C2L2) achieved the highest accuracy of (86.55%) with the polynomial kernel of the SVM classifier, surpassing the Fast Line-of-Sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) at (85.31%) and Collection 2 Level 1 (C2L1) at (84.93%). The LS8 data exhibits similar behavior. Conversely, when using the maximum-likelihood classifier, the highest accuracy (83.06%) was achieved with FLAASH. The results demonstrate significant variations in accuracies for different land cover classes, which emphasizes the importance of per-class accuracy. The results highlight the critical role of preprocessing techniques and classifier selection in optimizing the classification processes and land cover mapping accuracy for remote sensing geospatial applications. Finally, the actual differences in classification accuracy between processing levels are larger than those given by the confusion matrix. So, the consideration of alternative evaluation methods with the absence of reference images is critical. https://jih.uobaghdad.edu.iq/index.php/j/article/view/3992support vector machineremote sensing atmospheric correctionLandsat 9classification. |
| spellingShingle | Nehad Al-Salmany Taghreed A. Naji Assessing Landsat Processing Levels and Support Vector Machine Classification Ibn Al-Haitham Journal for Pure and Applied Sciences support vector machine remote sensing atmospheric correction Landsat 9 classification. |
| title | Assessing Landsat Processing Levels and Support Vector Machine Classification |
| title_full | Assessing Landsat Processing Levels and Support Vector Machine Classification |
| title_fullStr | Assessing Landsat Processing Levels and Support Vector Machine Classification |
| title_full_unstemmed | Assessing Landsat Processing Levels and Support Vector Machine Classification |
| title_short | Assessing Landsat Processing Levels and Support Vector Machine Classification |
| title_sort | assessing landsat processing levels and support vector machine classification |
| topic | support vector machine remote sensing atmospheric correction Landsat 9 classification. |
| url | https://jih.uobaghdad.edu.iq/index.php/j/article/view/3992 |
| work_keys_str_mv | AT nehadalsalmany assessinglandsatprocessinglevelsandsupportvectormachineclassification AT taghreedanaji assessinglandsatprocessinglevelsandsupportvectormachineclassification |