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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/40322

Title: Bathymetric mapping in Dong-Sha Atoll using SPOT data
Authors: Huang, Shih-Jen
Wen, Yao-Chung
Contributors: 國立臺灣海洋大學:海洋環境資訊學系
Keywords: unsupervised classification
SPOT
Dong-Sha atoll
bathymetry
Date: 2006-11
Issue Date: 2017-01-17T06:40:20Z
Publisher: Proceedings of ISRS 2006 PORSEC
Abstract: Abstract: The remote sensing data can be used to calculate the water depth especially in the clear and shallow water area. In this study, the SPOT data was used for bathymetric mapping in Dong-Sha atoll, located in northern South China Sea. The in situ sea depth was collected by echo sounder as well. A global positioning system was employed to locate the accurate sampling points for sea depth. An empirical model between measurement sea depth and band digital count was determined and based on least squares regression analysis. Both non-classification and unsupervised classification were used in this study. The results show that the standard error is less than 0.9m for non-classification. Besides, the 10% error related to the measurement water depth can be satisfied for more than 85% in situ data points. Otherwise, the 10% relative error can reach more than 97%, 69%, and 51% data points at class 4, 5, and 6 respectively if supervised classification is applied. Meanwhile, we also find that the unsupervised classification can get more accuracy to estimate water depth with standard error less than 0.63, 0.93, and 0.68m at class 4, 5, and 6 respectively.
Relation: 2
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/40322
Appears in Collections:[海洋環境資訊系] 期刊論文

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