Coastal Image Classification and Pattern Recognition: Tairua Beach, New Zealand
The study of coastal processes is critical for the protection and development of beach amenities, infrastructure, and properties. Many studies of beach evolution rely on data collected using remote sensing and show that beach evolution can be characterized by a finite number of “beach states”. Howev...
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MDPI AG
2021-11-01
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| Online Access: | https://www.mdpi.com/1424-8220/21/21/7352 |
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| author | Bo Liu Bin Yang Sina Masoud-Ansari Huina Wang Mark Gahegan |
| author_facet | Bo Liu Bin Yang Sina Masoud-Ansari Huina Wang Mark Gahegan |
| author_sort | Bo Liu |
| collection | DOAJ |
| description | The study of coastal processes is critical for the protection and development of beach amenities, infrastructure, and properties. Many studies of beach evolution rely on data collected using remote sensing and show that beach evolution can be characterized by a finite number of “beach states”. However, due to practical constraints, long-term data displaying all beach states are rare. Additionally, when the dataset is available, the accuracy of the classification is not entirely objective since it depends on the operator. To address this problem, we collected hourly coastal images and corresponding tidal data for more than 20 years (November 1998–August 2019). We classified the images into eight categories according to the classic beach state classification, defined as (1) reflective, (2) incident scaled bar, (3) non-rhythmic, attached bar, (4) attached rhythmic bar, (5) offshore rhythmic bar, (6) non-rhythmic, 3-D bar, (7) infragravity scaled 2-D bar, (8) dissipative. We developed a classification model based on convolutional neural networks (CNN). After image pre-processing with data enhancement, we compared different CNN models. The improved ResNext obtained the best and most stable classification with <i>F1</i>-score of 90.41% and good generalization ability. The classification results of the whole dataset were transformed into time series data. MDLats algorithms were used to find frequent temporal patterns in morphology changes. Combining the pattern of coastal morphology change and the corresponding tidal data, we also analyzed the characteristics of beach morphology and the changes in morphodynamic states. |
| first_indexed | 2024-03-10T05:51:46Z |
| format | Article |
| id | doaj.art-38fde0fed4da436ea79965ced0f762ac |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T05:51:46Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-38fde0fed4da436ea79965ced0f762ac2023-11-22T21:40:20ZengMDPI AGSensors1424-82202021-11-012121735210.3390/s21217352Coastal Image Classification and Pattern Recognition: Tairua Beach, New ZealandBo Liu0Bin Yang1Sina Masoud-Ansari2Huina Wang3Mark Gahegan4School of Software Engineering, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, ChinaSchool of Software Engineering, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, ChinaCentre for e-Research, The University of Auckland, Auckland 1010, New ZealandSchool of Software Engineering, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, ChinaCentre for e-Research, The University of Auckland, Auckland 1010, New ZealandThe study of coastal processes is critical for the protection and development of beach amenities, infrastructure, and properties. Many studies of beach evolution rely on data collected using remote sensing and show that beach evolution can be characterized by a finite number of “beach states”. However, due to practical constraints, long-term data displaying all beach states are rare. Additionally, when the dataset is available, the accuracy of the classification is not entirely objective since it depends on the operator. To address this problem, we collected hourly coastal images and corresponding tidal data for more than 20 years (November 1998–August 2019). We classified the images into eight categories according to the classic beach state classification, defined as (1) reflective, (2) incident scaled bar, (3) non-rhythmic, attached bar, (4) attached rhythmic bar, (5) offshore rhythmic bar, (6) non-rhythmic, 3-D bar, (7) infragravity scaled 2-D bar, (8) dissipative. We developed a classification model based on convolutional neural networks (CNN). After image pre-processing with data enhancement, we compared different CNN models. The improved ResNext obtained the best and most stable classification with <i>F1</i>-score of 90.41% and good generalization ability. The classification results of the whole dataset were transformed into time series data. MDLats algorithms were used to find frequent temporal patterns in morphology changes. Combining the pattern of coastal morphology change and the corresponding tidal data, we also analyzed the characteristics of beach morphology and the changes in morphodynamic states.https://www.mdpi.com/1424-8220/21/21/7352coastal imageconvolutional neural networksbeach state classificationpattern recognition |
| spellingShingle | Bo Liu Bin Yang Sina Masoud-Ansari Huina Wang Mark Gahegan Coastal Image Classification and Pattern Recognition: Tairua Beach, New Zealand Sensors coastal image convolutional neural networks beach state classification pattern recognition |
| title | Coastal Image Classification and Pattern Recognition: Tairua Beach, New Zealand |
| title_full | Coastal Image Classification and Pattern Recognition: Tairua Beach, New Zealand |
| title_fullStr | Coastal Image Classification and Pattern Recognition: Tairua Beach, New Zealand |
| title_full_unstemmed | Coastal Image Classification and Pattern Recognition: Tairua Beach, New Zealand |
| title_short | Coastal Image Classification and Pattern Recognition: Tairua Beach, New Zealand |
| title_sort | coastal image classification and pattern recognition tairua beach new zealand |
| topic | coastal image convolutional neural networks beach state classification pattern recognition |
| url | https://www.mdpi.com/1424-8220/21/21/7352 |
| work_keys_str_mv | AT boliu coastalimageclassificationandpatternrecognitiontairuabeachnewzealand AT binyang coastalimageclassificationandpatternrecognitiontairuabeachnewzealand AT sinamasoudansari coastalimageclassificationandpatternrecognitiontairuabeachnewzealand AT huinawang coastalimageclassificationandpatternrecognitiontairuabeachnewzealand AT markgahegan coastalimageclassificationandpatternrecognitiontairuabeachnewzealand |