A Novel Acoustic Sediment Classification Method Based on the K-Mdoids Algorithm Using Multibeam Echosounder Backscatter Intensity
نویسندگان
چکیده
The modern discrimination of sediment is based on acoustic intensity (backscatter) information from high-resolution multibeam echo-sounder systems (MBES). backscattering intensity, varying with the angle incidence, reveals characteristics seabed sediment. In this study, we propose a novel unsupervised classification method K-medoids algorithm using data. method, use Lurton parameters model, which relationship between and to obtain corresponding curve, genetic fit curve by least-squares method. After extracting four relevant model when ideal fitting effect was achieved, input characteristic obtained clustering model. To validate proposed compare it self-organizing map (SOM) neural network under same parameter settings. results experiment show that category less than or equal 3, SOM are approximately identical. As increases, shows instability, impossible see clear boundaries sediment, while 5 correct. comparing field in situ sampling along MBES survey line, consistent distribution sampling. accuracies for bedrock, sandy clay, silty sand all above 90%; those gravel clay nearly 80%, overall accuracy reaches 89.7%.
منابع مشابه
Delft University of Technology Performance of Multibeam Echosounder Backscatter-Based Classification for Monitoring Sediment Distributions Using Multitemporal Large-Scale Ocean Data Sets
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ژورنال
عنوان ژورنال: Journal of Marine Science and Engineering
سال: 2021
ISSN: ['2077-1312']
DOI: https://doi.org/10.3390/jmse9050508