1Magister Student in Marine Technology, Department of Marine Science and Technology, Faculty of Fisheries and Marine Science, IPB University, Indonesia
2Department of Marine Science and Technology, Faculty of Fisheries and Marine Science,, Indonesia
3IPB University, Indonesia
BibTex Citation Data :
@article{JKT16050, author = {Muhammad Prasetya and Vincentius Paulus Siregar and Syamsul Bahri Agus}, title = {Comparison of Satellite-Derived Bathymetry Algorithm Accuracy Using Sentinel-2 Multispectral Satellite Image}, journal = {Jurnal Kelautan Tropis}, volume = {26}, number = {1}, year = {2023}, keywords = {bathymetry; Lyzenga; remote sensing; Stumpf; Support Vector Machine}, abstract = { The utilization of satellite image data and image data processing techniques has become an efficient alternative to obtain bathymetric data in a broad and complicated area. This study aimed to determine the algorithm's performance in the waters of Lambasina Island. Atmospheric and radiometric correction using the Dark Object Subtraction (DOS) method for initial processing of Sentinel-2 images. The multispectral channel used, namely the blue, green, and red bands, was tested by regression using field observation data. The algorithms used to estimate bathymetry include Lyzenga, Stumpf, and Support Vector Machine (SVM). The test results of the three algorithms show ed that the support vector machine algorithm wa s the best algorithm for estimating bathymetry after the Stumpf and Lyzenga algorithms. The correlation results of the SVM algorithm in the waters of the small Lambasina island g o t a correlation coefficient of determination R 2 = 0.81 and the large Lambasina waters area R 2 = 0.82. The second-best algorithm wa s Stumpf, with a correlation coefficient of determination of R 2 = 0.79 in the waters of the small Lambasina island and R 2 = 0.80 in the waters of the large Lambasina island. Lyzenga's algorithm g o t the correlation coefficient of determination R 2 = 0.78 on small Lambasina Islands and large Lambasina Islands with a determination correlation coefficient value of R 2 = 0.79. }, issn = {2528-3111}, pages = {113--125} doi = {10.14710/jkt.v26i1.16050}, url = {https://ejournal2.undip.ac.id/index.php/jkt/article/view/16050} }
Refworks Citation Data :
The utilization of satellite image data and image data processing techniques has become an efficient alternative to obtain bathymetric data in a broad and complicated area. This study aimed to determine the algorithm's performance in the waters of Lambasina Island. Atmospheric and radiometric correction using the Dark Object Subtraction (DOS) method for initial processing of Sentinel-2 images. The multispectral channel used, namely the blue, green, and red bands, was tested by regression using field observation data. The algorithms used to estimate bathymetry include Lyzenga, Stumpf, and Support Vector Machine (SVM). The test results of the three algorithms showed that the support vector machine algorithm was the best algorithm for estimating bathymetry after the Stumpf and Lyzenga algorithms. The correlation results of the SVM algorithm in the waters of the small Lambasina island got a correlation coefficient of determination R2 = 0.81 and the large Lambasina waters area R2 = 0.82. The second-best algorithm was Stumpf, with a correlation coefficient of determination of R2 = 0.79 in the waters of the small Lambasina island and R2 = 0.80 in the waters of the large Lambasina island. Lyzenga's algorithm got the correlation coefficient of determination R2 = 0.78 on small Lambasina Islands and large Lambasina Islands with a determination correlation coefficient value of R2 = 0.79.
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