Seabed Sediment Classification Using Spatial Statistical Characteristics
Conventional sediment classification methods based on Multibeam Echo System (MBES) data have low accuracy since the correlation between features and sediment has not been fully considered. Moreover, their poor resistance to the residual error of MBES backscatter strength (BS) processing also degrade...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-05-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/10/5/691 |
| _version_ | 1797498759081361408 |
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| author | Quanyin Zhang Jianhu Zhao Shaobo Li Hongmei Zhang |
| author_facet | Quanyin Zhang Jianhu Zhao Shaobo Li Hongmei Zhang |
| author_sort | Quanyin Zhang |
| collection | DOAJ |
| description | Conventional sediment classification methods based on Multibeam Echo System (MBES) data have low accuracy since the correlation between features and sediment has not been fully considered. Moreover, their poor resistance to the residual error of MBES backscatter strength (BS) processing also degrades their performances. Toward these problems, we propose a seabed sediment classification method using spatial statistical features extracted from angular response curve (ARC), topography, and geomorphology. First, to reduce interference of noise and residual error of beam pattern correction, we propose a robust method combining the Generic Seafloor Acoustic Backscatter (GSAB) model and Huber loss function to estimate the parameters of ARC which is strongly correlated with seabed sediments. Second, a feature set is constructed by AR features composed of GSAB parameters, BS mosaic and its derivatives, and seabed topography and its derivatives to characterize seabed sediments. After that, feature selection and probability map acquisition are employed based on the random forest algorithm (RF). Finally, a denoising and final sediment map generation method is proposed and applied to probability maps to obtain the sediment map with reasonable sediment distribution and clear boundaries between classes. We implement experiments and achieve the classification accuracy of 93.3%, which verifies the validity of our method. |
| first_indexed | 2024-03-10T03:37:57Z |
| format | Article |
| id | doaj.art-c3a40210068f44d38ecdcfe192482d84 |
| institution | Directory Open Access Journal |
| issn | 2077-1312 |
| language | English |
| last_indexed | 2024-03-10T03:37:57Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj.art-c3a40210068f44d38ecdcfe192482d842023-11-23T11:40:27ZengMDPI AGJournal of Marine Science and Engineering2077-13122022-05-0110569110.3390/jmse10050691Seabed Sediment Classification Using Spatial Statistical CharacteristicsQuanyin Zhang0Jianhu Zhao1Shaobo Li2Hongmei Zhang3School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, ChinaSchool of Geodesy and Geomatics, Wuhan University, Wuhan 430079, ChinaSchool of Geodesy and Geomatics, Wuhan University, Wuhan 430079, ChinaDepartment of Artificial Intelligence and Automation, School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, ChinaConventional sediment classification methods based on Multibeam Echo System (MBES) data have low accuracy since the correlation between features and sediment has not been fully considered. Moreover, their poor resistance to the residual error of MBES backscatter strength (BS) processing also degrades their performances. Toward these problems, we propose a seabed sediment classification method using spatial statistical features extracted from angular response curve (ARC), topography, and geomorphology. First, to reduce interference of noise and residual error of beam pattern correction, we propose a robust method combining the Generic Seafloor Acoustic Backscatter (GSAB) model and Huber loss function to estimate the parameters of ARC which is strongly correlated with seabed sediments. Second, a feature set is constructed by AR features composed of GSAB parameters, BS mosaic and its derivatives, and seabed topography and its derivatives to characterize seabed sediments. After that, feature selection and probability map acquisition are employed based on the random forest algorithm (RF). Finally, a denoising and final sediment map generation method is proposed and applied to probability maps to obtain the sediment map with reasonable sediment distribution and clear boundaries between classes. We implement experiments and achieve the classification accuracy of 93.3%, which verifies the validity of our method.https://www.mdpi.com/2077-1312/10/5/691acoustic sediment classificationangular responseprobability map filterrandom forest |
| spellingShingle | Quanyin Zhang Jianhu Zhao Shaobo Li Hongmei Zhang Seabed Sediment Classification Using Spatial Statistical Characteristics Journal of Marine Science and Engineering acoustic sediment classification angular response probability map filter random forest |
| title | Seabed Sediment Classification Using Spatial Statistical Characteristics |
| title_full | Seabed Sediment Classification Using Spatial Statistical Characteristics |
| title_fullStr | Seabed Sediment Classification Using Spatial Statistical Characteristics |
| title_full_unstemmed | Seabed Sediment Classification Using Spatial Statistical Characteristics |
| title_short | Seabed Sediment Classification Using Spatial Statistical Characteristics |
| title_sort | seabed sediment classification using spatial statistical characteristics |
| topic | acoustic sediment classification angular response probability map filter random forest |
| url | https://www.mdpi.com/2077-1312/10/5/691 |
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