IMAGE PRE-PROCESSING STRATEGIES for ENHANCING PHOTOGRAMMETRIC 3D RECONSTRUCTION of UNDERWATER SHIPWRECK DATASETS

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Abstract

Although underwater photogrammetry has become widely adopted, there are still significant unresolved issues that are worthy of attention. This article focuses on the 3D model generation of underwater shipwrecks and intends explicitly to address the problem of dealing with sub-optimal datasets. Even if the definition of best practices and standards to be adopted during the acquisition phase appears to be crucial, there is a massive amount of data gathered so far by professionals and the scientific community all over the world that cannot be ignored. The compelling idea is to attempt to achieve the best reconstruction results possible, even from sub-optimal or less-than-ideal image datasets. This work focuses on the investigation of different strategies and approaches for balancing the quality of the photogrammetric products, without neglecting their reliability concerning the surveyed object. The case study of this research is the Mandalay MHT, a 34 m long steel-hulled auxiliary schooner that sank in 1966 and now lies in the Biscayne National Park (Florida - USA). The dataset has been provided by the Submerged Resources Center (SRC) of the US National Park Service, in order to develop an experimental image enhancement method functional to the virtualization and visualization of the generated products, as a part of a sustainable, affordable, and reliable method of studying submerged artefacts and sites. The original images have been processed using different image enhancement approaches, and the outputs have been compared and analysed.

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Calantropio, A., Chiabrando, F., Seymour, B., Kovacs, E., Lo, E., & Rissolo, D. (2020). IMAGE PRE-PROCESSING STRATEGIES for ENHANCING PHOTOGRAMMETRIC 3D RECONSTRUCTION of UNDERWATER SHIPWRECK DATASETS. In International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives (Vol. 43, pp. 941–948). International Society for Photogrammetry and Remote Sensing. https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-941-2020

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