Abstract
Abstract. The work presented in this paper investigates the effect of the radiometry of the underwater imagery on automating the 3D reconstruction and the produced orthoimagery. Main aim is to investigate whether pre-processing of the underwater imagery improves the 3D reconstruction using automated SfM - MVS software or not. Since the processing of images either separately or in batch is a time-consuming procedure, it is critical to determine the necessity of implementing colour correction and enhancement before the SfM - MVS procedure or directly to the final orthoimage when the orthoimagery is the deliverable. Two different test sites were used to capture imagery ensuring different environmental conditions, depth and complexity. Three different image correction methods are applied: A very simple automated method using Adobe Photoshop, a developed colour correction algorithm using the CLAHE (Zuiderveld, 1994) method and an implementation of the algorithm described in Bianco et al., (2015). The produced point clouds using the initial and the corrected imagery are then being compared and evaluated.
Highlights
Underwater 3D modelling and mapping techniques are based on various systems and methodologies and most of these methods are based on RGB imagery as primary data (Drap 2012, Henderson et al, 2013, Johnson‐Roberson et al, 2016)
Three different image correction methods are applied to these datasets: A very simple automated method using Adobe Photoshop, a developed colour correction algorithm using the Contrast Limited AHE (CLAHE) (Zuiderveld, 1994) method and an implementation of the algorithm described in Bianco et al, (2015)
The orthoimages created using the original uncorrected imagery were corrected using the aforementioned algorithms. By comparing both the visual appearance and the histograms of the results illustrated in Figures 6-7 and Figures 9-10, occurs that the implementation of the specific image enhancement techniques do not affect in a remarkable way the produced orthoimagery
Summary
Underwater 3D modelling and mapping techniques are based on various systems and methodologies and most of these methods are based on RGB imagery as primary data (Drap 2012, Henderson et al, 2013, Johnson‐Roberson et al, 2016). Despite the relative low cost of these methods in relation to others, they present a major drawback; optical properties and illumination conditions of water severely affect underwater imagery. Colours are lost as the depth increases, resulting in a green-blue image due to light absorption, which affects mainly red wavelength. The recovery of the correct or at least realistic underwater colour imagery is a very challenging and promising research field which affects the 3D modelling and mapping techniques. To overcome these problems, two different approaches for underwater image processing are found in the literature
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