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.

Regions around the world experience adverse climate change induced conditions which pose severe risks to the normal and sustainable operations of modern societies. Extreme weather events, such as floods, rising sea-levels and storms, stand as characteristic examples that impair the core services of the global ecosystem. Especially floods have a severe impact on human activities, hence early and accurate delineation of the disaster is of top-priority since it provides environmental, economic, and societal benefits and eases relief efforts. In this work, we introduce OmbriaNet, a deep neural network architecture, based on Convolutional Neural Networks (CNNs), that detects changes between permanent and flooded water areas by exploiting the temporal differences among flood events extracted by different sensors. To demonstrate the potential of the proposed approach, we generated OMBRIA, a bitemporal and multimodal satellite imagery dataset for image segmentation through supervised binary classification. It consists of a total number of 3.376 images, Synthetic Aperture Radar (SAR) imagery from Sentinel-1, and multispectral imagery from Sentinel-2, accompanied with ground truth binary images produced from data derived by experts and provided from the Emergency Management Service of the European Space Agency Copernicus Program. The dataset covers 23 flood events around the globe, from 2017 to 2021. We collected, co-registrated and pre-processed the data in Google Earth Engine. To validate the performance of our method, we performed different benchmarking experiments on the OMBRIA dataset and we compared with several competitive state-of-theart techniques. The experimental analysis demonstrated that the proposed formulation is able to produce high-quality flood maps, achieving a superior performance over the state-of-theart.We provide OMBRIA dataset, as well as OmbriaNet code at: https://github.com/geodrak/OMBRIA.

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