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• W4361010673 abstract "Floods can cause huge damage to society, the economy, and the environment. As a result, it is vital to determine the extent and type of land cover in flooded areas quickly and accurately in order to facilitate disaster relief and mitigation efforts. Synthetic aperture radar (SAR) is an all-weather, 24 h data source used to extract information about flood inundations, and its primary aim is to extract water body information for flood monitoring. In this study, we have studied the backscattering characteristics of water and non-water, combined the threshold segmentation method with Markov random fields (MRF), and embedded simulated annealing (SA) in the process of image noise reduction, resulting in the development of a water extraction method KI-MRF-SA with high accuracy in classification and high automation. Furthermore, object-scale adaptive convolutional neural networks (OSA-CNN) are introduced for the classification of optical images before the flood in order to provide reference data for flood inundation analysis. The method proposed in this study consists of the following three steps: (1) The Kittler and Illingworth (KI) thresholding algorithm is used for the segmentation of SAR images in order to determine the initial flood inundation extent; (2) MRF and SA algorithms are employed as a means to optimize the initial flood inundation extent, and the results are combined across multiple polarizations by using an intersection operation to determine the final flood inundation extent; and (3) As part of the flood mapping process, land cover types before the flood are classified using OSA-CNN and combined with flood inundation extents. According to the experimental results, it is evident that the proposed KI-MRF-SA method is capable of distinguishing water from non-water with significantly higher accuracy (3–5% improvement in the overall accuracy) than conventional thresholding methods. Combined with the classification method of OSA-CNN proposed in our earlier research, the overall classification accuracy of flood-affected areas could reach 92.7%." @default.
• W4361010673 created "2023-03-30" @default.
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• W4361010673 date "2023-03-24" @default.
• W4361010673 modified "2023-09-25" @default.
• W4361010673 title "Extraction and Classification of Flood-Affected Areas Based on MRF and Deep Learning" @default.
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• W4361010673 doi "https://doi.org/10.3390/w15071288" @default.
• W4361010673 hasPublicationYear "2023" @default.
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