Bathymetric inversion using multispectral imagery is an effective way to obtain shallow bathymetric information in water but is with relatively low accuracy. This study focuses on the solid disturbance of shallow seafloor substrate variation and proposes an image-segmentation-based method to improve the shallow bathymetry retrieval accuracy. The image is partitioned into different subregions with homogenous substrate properties, and the bathymetric inversion model is constructed separately in each subregion, thus improving the retrieval accuracy. Experimental results of the Ganquan Island region show that the accuracy of the bathymetric inversion was enhanced by 58.1% after image segmentation using muti statistic features.
Abyssal topographic information is critical for abyssal scientific research. The ship-borne multi-beam system efficiently measures deep-water seafloor but has low precision and resolution. By contrast, a muti-beam system mounted on AUV can obtain precise depth data and effectively supplement ship-borne multi-beam data. When combining datasets from these two platforms, improved spatiotemporal coverage can characterize the abyssal seafloor change. However, the AUV platform fixes its position using acoustic methods, which introduces a significant bias in its positioning reference. Consequently, the depth variation can be misinterpreted when combining depth data from ship-borne and AUV platforms. In this paper, the robust terrain matching method is used to register the data of different platforms. The method was tested on the Juan de Fuca Ridge datasets. Computational results demonstrate that the proposed method eliminates the positioning reference bias effectively and thus improves the accuracy of the interpretation of the abyssal seafloor change.
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