Underwater Linear Feature Extraction with Multispectral Band Images: An Evaluation with Level-set Method in Dongsha Atoll and Zengmu Shoal / 利用水平集方法擷取水下特徵-以東沙環礁及曾母暗沙為例

碩士 / 國立交通大學 / 土木工程系所 / 103 / Optical remote sensing satellite provides a useful and convenient source for identifying underwater features particularly for shallow water area. This capability varies with wavelength. In this study, the capability of finding underwater features with different bands of multispectral image.
There are two study sites in this study. The first site is Dongsha atoll, which is composed of Dongsha island, lagoon, and surrounding reefs. The water depth ranges from zero to less than three 3m at outer ring and 20m in the lagoon. The images were acquired with WorldView-2 in October of 2013. The second site is Zengmu shoal, which is completely an underwater feature. The image used is a scene acquired with Landsat 8. The water depth ranges from 17 to 25 meter. The water clarity is high in both areas. The difference of these two study sites is that Dongsha atoll contains more underwater features than Zengmu shoal.
For the Dongsha case, two sub areas are selected. Besides the original bands, NDWI and principle component transformation image are analyzed as well. The assessment is made with the number of segments identified. The more segments identified would be taken as providing more information. From the result, the first principle component performs the best, and then is NDWIWV2. It means that band combination provides more information than a single band. For the Zengmu shoal case, the boundary from segmentation is compared with manually digitized. Among the spectral bands, C/A and blue performs the best. And the first component of the Principle Components from Coastal, Blue, Green, Red bands, performs the best. The boundary of Zengmu shoal is found to be close to the -30m contour line.

Identiferoai:union.ndltd.org:TW/103NCTU5015095
Date January 2015
CreatorsLin, Jian-Wei, 林建維
ContributorsShih, Tian - Yuan, 史天元
Source SetsNational Digital Library of Theses and Dissertations in Taiwan
Languagezh-TW
Detected LanguageEnglish
Type學位論文 ; thesis
Format69

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