Support vector machine and decision tree based classification of side-scan sonar mosaics using textural features
The diversity and heterogeneity of coastal, estuarine and stream habitats has led to them becoming a prevalent topic for study. Woody ruins are areas of potential riverbed habitat, particularly for fish. Therefore, the mapping of those areas is of interest. However, due to the limited visibility in...
| Main Authors: | , , |
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| Format: | Conference Paper |
| Published: |
2019
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| Online Access: | http://hdl.handle.net/20.500.11937/80031 |
| _version_ | 1848764144226926592 |
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| author | Febriawan, H.K. Helmholz, Petra Parnum, Iain |
| author_facet | Febriawan, H.K. Helmholz, Petra Parnum, Iain |
| author_sort | Febriawan, H.K. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | The diversity and heterogeneity of coastal, estuarine and stream habitats has led to them becoming a prevalent topic for study. Woody ruins are areas of potential riverbed habitat, particularly for fish. Therefore, the mapping of those areas is of interest. However, due to the limited visibility in some river systems, satellites, airborne or other camera-based systems (passive systems) cannot be used. By contrast, sidescan sonar is a popular underwater acoustic imaging system that is capable of providing high-resolution monochromatic images of the seafloor and riverbeds. Although the study of sidescan sonar imaging using supervised classification has become a prominent research subject, the use of composite texture features in machine learning classification is still limited. This study describes an investigation of the use of texture analysis and feature extraction on side-scan sonar imagery in two supervised machine learning classifications: Support Vector Machine (SVM) and Decision Tree (DT). A combination of first-order texture and second-order texture is investigated to obtain the most appropriate texture features for the image classification. SVM, using linear and Gaussian kernels along with Decision Tree classifiers, was examined using selected texture features. The results of overall accuracy and kappa coefficient revealed that SVM using a linear kernel leads to a more promising result, with 77% overall accuracy and 0.62 kappa, than SVM using either a Gaussian kernel or Decision Tree (60% and 73% overall accuracy, and 0.39 and 0.59 kappa, respectively). However, this study has demonstrated that SVM using linear and Gaussian kernels as well as a Decision Tree makes it capable of being used in side-scan sonar image classification and riverbed habitat mapping. |
| first_indexed | 2025-11-14T11:14:41Z |
| format | Conference Paper |
| id | curtin-20.500.11937-80031 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:14:41Z |
| publishDate | 2019 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-800312021-01-04T05:39:58Z Support vector machine and decision tree based classification of side-scan sonar mosaics using textural features Febriawan, H.K. Helmholz, Petra Parnum, Iain The diversity and heterogeneity of coastal, estuarine and stream habitats has led to them becoming a prevalent topic for study. Woody ruins are areas of potential riverbed habitat, particularly for fish. Therefore, the mapping of those areas is of interest. However, due to the limited visibility in some river systems, satellites, airborne or other camera-based systems (passive systems) cannot be used. By contrast, sidescan sonar is a popular underwater acoustic imaging system that is capable of providing high-resolution monochromatic images of the seafloor and riverbeds. Although the study of sidescan sonar imaging using supervised classification has become a prominent research subject, the use of composite texture features in machine learning classification is still limited. This study describes an investigation of the use of texture analysis and feature extraction on side-scan sonar imagery in two supervised machine learning classifications: Support Vector Machine (SVM) and Decision Tree (DT). A combination of first-order texture and second-order texture is investigated to obtain the most appropriate texture features for the image classification. SVM, using linear and Gaussian kernels along with Decision Tree classifiers, was examined using selected texture features. The results of overall accuracy and kappa coefficient revealed that SVM using a linear kernel leads to a more promising result, with 77% overall accuracy and 0.62 kappa, than SVM using either a Gaussian kernel or Decision Tree (60% and 73% overall accuracy, and 0.39 and 0.59 kappa, respectively). However, this study has demonstrated that SVM using linear and Gaussian kernels as well as a Decision Tree makes it capable of being used in side-scan sonar image classification and riverbed habitat mapping. 2019 Conference Paper http://hdl.handle.net/20.500.11937/80031 10.5194/isprs-archives-XLII-2-W13-27-2019 https://creativecommons.org/licenses/by/4.0/ fulltext |
| spellingShingle | Febriawan, H.K. Helmholz, Petra Parnum, Iain Support vector machine and decision tree based classification of side-scan sonar mosaics using textural features |
| title | Support vector machine and decision tree based classification of side-scan sonar mosaics using textural features |
| title_full | Support vector machine and decision tree based classification of side-scan sonar mosaics using textural features |
| title_fullStr | Support vector machine and decision tree based classification of side-scan sonar mosaics using textural features |
| title_full_unstemmed | Support vector machine and decision tree based classification of side-scan sonar mosaics using textural features |
| title_short | Support vector machine and decision tree based classification of side-scan sonar mosaics using textural features |
| title_sort | support vector machine and decision tree based classification of side-scan sonar mosaics using textural features |
| url | http://hdl.handle.net/20.500.11937/80031 |