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Performance of different classifiers for marine habitat mapping using side scan sonar and object-based image analysis

Rusmadi, Raihanah and Che Hasan, Rozaimi (2020) Performance of different classifiers for marine habitat mapping using side scan sonar and object-based image analysis. In: 10th IGRSM International Conference and Exhibition on Geospatial and Remote, IGRSM 2020, 20 October 2020 - 21 October 2020, Kuala Lumpur, Virtual, Malaysia.


Official URL: http://dx.doi.org/10.1088/1755-1315/540/1/012087


Acoustic sonar techniques have been one of the successful underwater mapping alternatives for identifying the seafloor features. The integration between the technique and classification analysis can produce detail map of the seafloor. Among these sonar technologies, side-scan sonar (SSS) is one of the tools for underwater mapping that can provide high spatial resolution seafloor mosaic which is presented in greyscale level. However, before it can be used for the coral reef marine habitat mapping, it is essential to properly assess its performance and quantify the amount of information that can be extracted. The objective of this study is to determine the accuracy of habitat maps derived using side scan sonar data, Object-based Image Analysis (OBIA) and five different classifier algorithms; Support Vector Machine (SVM), Random Forest (RF), k-Nearest Neighbour (k-NN), Decision Tree, and Bayes. This study utilized side-scan sonar model Klein system 3000 which operated at 100kHz combined with video data that was conducted in shallow water (depth > 10m). First, eight (8) texture layers were derived from side scan sonar mosaic using GLCM technique. Then, the GLCM layers of texture features were reduced using Principal Component Analysis (PCA) and analysed to seek for the most contributed texture layers. A total of 80 samples were derived which consist of four (4) classes; coral, sand, silt and mud. The result shows that the Support Vector Machine (SVM) method produced the highest accuracy which is 81.25% followed by k-Nearest Neighbours (k-NN), Random Forest (RF), Decision Tree and Bayes (68.75%, 66.25%, 57.5% and 45% respectively). The used of OBIA with SSS data offers a promising method to map marine habitats for a better understanding of spatial distribution and monitoring habitat changes in the future.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:marine habitat mapping, object based image analysis
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > T Technology (General) > T58.5-58.64 Information technology
Divisions:Razak School of Engineering and Advanced Technology
ID Code:93210
Deposited By: Yanti Mohd Shah
Deposited On:19 Nov 2021 11:29
Last Modified:19 Nov 2021 11:29

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