Bathymetry at shallow water especially shallower than 15m is an important area for environmental monitoring and
national defense. Because the depth of shallow water is changeable by the sediment deposition and the ocean waves, the
periodic monitoring at shoe area is needed. Utilization of satellite images are well matched for widely and repeatedly
monitoring at sea area. Sea bottom terrain model using by remote sensing data have been developed and these methods
based on the radiative transfer model of the sun irradiance which is affected by the atmosphere, water, and sea bottom.
We adopted that general method of the sea depth extraction to the satellite imagery, WorldView-2; which has very fine
spatial resolution (50cm/pix) and eight bands at visible to near-infrared wavelengths. From high-spatial resolution
satellite images, there is possibility to know the coral reefs and the rock area’s detail terrain model which offers
important information for the amphibious landing. In addition, the WorldView-2 satellite sensor has the band at near the
ultraviolet wavelength that is transmitted through the water. On the other hand, the previous study showed that the
estimation error by the satellite imagery was related to the sea bottom materials such as sand, coral reef, sea alga, and
rocks. Therefore, in this study, we focused on sea bottom materials, and tried to improve the depth estimation accuracy.
First, we classified the sea bottom materials by the SVM method, which used the depth data acquired by multi-beam
sonar as supervised data. Then correction values in the depth estimation equation were calculated applying the
classification results. As a result, the classification accuracy of sea bottom materials was 93%, and the depth estimation
error using the correction by the classification result was within 1.2m.
Yoriko Kazama and Tomonori Yamamoto, "Shallow water bathymetry correction using sea bottom classification with multispectral satellite imagery," Proc. SPIE 10422, Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2017, 1042208 (Presented at SPIE Remote Sensing: September 11, 2017; Published: 17 October 2017); https://doi.org/10.1117/12.2280305.
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