Coastal seabed mapping is essential for a variety of nearshore management related activities including sustainable resource management, ecological protection, and environmental change detection in coastal sites...In this study, a soft seabed classification (SSC) algorithm is developed using unsupervised classification with fuzzy clustering to produce classification products accounting for a sub-footprint habitat mixture. With this approach, each shot is assigned to multiple seabed classes with a percentage cover measuring the extent to which each seabed class is present in the ALB footprint. This has the added benefit of generating smooth spatial ecological transitions of the seabed instead of sharp boundaries between classes or clusters. Furthermore, due to the multivariate nature of the SSC output (i.e., percentage cover for each seabed class for a given shot), a recently developed selforganizing map neural network-based approach to geo-visualization of seabed classification results was used to visualize seabed habitat diversity. An ALB dataset of an area approximately
20000 m2 collected from Quebec, Canada was used. Cross-validation of the SSC approach yields percentage cover accuracy of approximately 71.7% with 16 seabed classes for a real ALB dataset, while dominant seabed class prediction based on hardening of percentage cover
predictions yielded 66% accuracy for 4 seabed classes.