Object-oriented classification of sidescan sonar data for mapping benthic marine habitats

This research presents an object-oriented technique for habitat classification at different segmentation levels based on the use of imagery from an Edgetech 272 side scan sonar. We investigate the success of object parameters such as shape and size as well as texture in discriminating reef from sand habitat. The results are evaluated using traditional digitization, based on visual assessment of the sidescan imagery, and video transects. Whereas the application of traditional pixel-based classification results in a pixelized (salt and pepper) representation of habitat distribution, the object-based classification technique results in habitat objects (raster or vector). The object-oriented classification results are cross-validated using confusion matrices in image classification software and error matrices from underwater video transects showing an overall accuracy of 80% based on two classes within the image at three segmentation levels and an overall accuracy of 60% based on three classes at two segmentation levels. This is compared with the digitized layer accuracy of 81% for two classes and 72% for three classes, and this demonstrates the successful application of object-oriented methods for habitat mapping. This technique retains spatially discrete habitat pattern information in a classified vector shape file with methods that are automated, repeatable, objective, and capable of processing many sidescan records in a more efficient manner.