{"refrec":{"BRefID":353630,"RR":"<b>Zhao, T.; Montereale-Gavazzi, G.; Lazendic, S.; Zhao, Y.; Pižurica, A.</b> (2021). Acoustic seafloor classification using the Weyl transform of multibeam echosounder backscatter mosaic. <i>Remote Sens. 13(9)</i>: 1760. <a href=\"https://dx.doi.org/10.3390/rs13091760\" target=\"_blank\">https://dx.doi.org/10.3390/rs13091760</a>","BEntID":351340,"PublicFlag":1,"CheckedFlag":1,"wosflag":1,"vabbflag":1,"RefStringPartII":". <i>Remote Sens. 13(9)</i>: 1760. <a href=\"https://dx.doi.org/10.3390/rs13091760\" target=\"_blank\">https://dx.doi.org/10.3390/rs13091760</a>","DocTypID":8,"DocType":"Journal article","MarineFlag":1,"FreshFlag":0,"BrackishFlag":0,"TerrestrialFlag":0,"Authorstring":"Zhao, T.; Montereale-Gavazzi, G.; Lazendic, S.; Zhao, Y.; Pižurica, A.","OrigTitleTranslFlag":0,"Authorstringtrunc":"Zhao, T. <i>et al.</i>","Englishabstract":"The use of multibeam echosounder systems (MBES) for detailed seafloor mapping is increasing at a fast pace. Due to their design, enabling continuous high-density measurements and the coregistration of seafloor’s depth and reflectivity, MBES has become a fundamental instrument in the advancing field of acoustic seafloor classification (ASC). With these data becoming available, recent seafloor mapping research focuses on the interpretation of the hydroacoustic data and automated predictive modeling of seafloor composition. While a methodological consensus on which seafloor sediment classification algorithm and routine does not exist in the scientific community, it is expected that progress will occur through the refinement of each stage of the ASC pipeline: ranging from the data acquisition to the modeling phase. This research focuses on the stage of the feature extraction; the stage wherein the spatial variables used for the classification are, in this case, derived from the MBES backscatter data. This contribution explored the sediment classification potential of a textural feature based on the recently introduced Weyl transform of 300 kHz MBES backscatter imagery acquired over a nearshore study site in Belgian Waters. The goodness of the Weyl transform textural feature for seafloor sediment classification was assessed in terms of cluster separation of Folk’s sedimentological categories (4-class scheme). Class separation potential was quantified at multiple spatial scales by cluster silhouette coefficients. Weyl features derived from MBES backscatter data were found to exhibit superior thematic class separation compared to other well-established textural features, namely: (1) First-order Statistics, (2) Gray Level Co-occurrence Matrices (GLCM), (3) Wavelet Transform and (4) Local Binary Pattern (LBP). Finally, by employing a Random Forest (RF) categorical classifier, the value of the proposed textural feature for seafloor sediment mapping was confirmed in terms of global and by-class classification accuracies, highest for models based on the backscatter Weyl features. Further tests on different backscatter datasets and sediment classification schemes are required to further elucidate the use of the Weyl transform of MBES backscatter imagery in the context of seafloor mapping.","AbstractOtherLang":null,"BibLvlCode":"AS","StandardTitle":"Acoustic seafloor classification using the Weyl transform of multibeam echosounder backscatter mosaic","OrigTitleLangCode":"en","OrigTitleLangCodeExtended":"eng","OrigTitleLangID":15,"DateLastModified":{"date":"2026-04-18 01:32:29.912976","timezone_type":1,"timezone":"+02:00"},"UserAccessRight":null,"UserAccID":null,"AuthorKeywords":"acoustic seafloor classification; multibeam backscatter imagery; Weyl transform; feature extraction; multiple scale; seafloor characterization","OtherDescriptors":null,"Notes":null,"AnaPub":2021,"MonPub":null,"DateUpdate":"2025-01-06","DateCreate":"2022-07-08","SecASFANote":null,"ConfID":null,"PeerRev":1,"VlizCoreFlag":1,"WoScode":"WOS:000650746200001","VABBcode":null,"OpenAcc":1,"DOI":"10.3390/rs13091760"},"refs":null,"anarec":{"AnaID":353630,"PubliDate":2021,"Pagination":"1760","XtraPublOfAnaID":null,"ISBN":null,"Volume":"13","Issue":"9","BRefMon":null,"BRefMonRR":null,"BRefXtra":null,"BRefXtraRR":null,"SerBRefID":221386,"SerRR":"Remote Sensing. 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