Abstract
Abstract. 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.
Highlights
Coastal, estuary and stream areas have diverse and heterogeneous habitats such as seagrass beds, mangrove, coral reefs and fish (Micallef et al, 2012; Mustajap et al, 2015)
A popular subject of research in sidescan sonar image analysis that can be found in several studies is use of the Grey Level Co-occurrence Matrix (GLCM) method to extract the second-order texture features (Lianantonakis & Petillot, 2007; Harrison et al, 2011; Hamilton, 2015; Buscombe, 2017; Hamill et al, 2018)
For the remaining tests six combinations of the textural variables used in the Support Vector Machine (SVM) classification which are shown in T able 1
Summary
Estuary and stream areas have diverse and heterogeneous habitats such as seagrass beds, mangrove, coral reefs and fish (Micallef et al, 2012; Mustajap et al, 2015). Understanding the spatial distribution of benthic habitat by providing maps of the substrates and seabed morphologies becomes essential in developing and managing river and estuary environments with ecosystem-based management strategies Underwater acoustic technologies, such as multibeam echo sounding (Kostylev et al, 2001; Parnum, 2007) and sidescan sonar (SSS) (Blondel, 2009; Lurton et al, 2015; Gutperlet et al, 2017) have been successful used for marine and estuarine habitat mapping; especially in turbid environments where optical-based methods can be ineffective. T he popular convolutional neural networks (CNN) have not been used for this research due to the limited training samples available
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