Soft classification of mixed seabed objects based on fuzzy clustering analysis using airborne LIDAR bathymetry data

摘要

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. Recently introduced airborne LIDAR bathymetry (ALB) sensors allow, under favorable environmental conditions and mapping requirements, time and cost efficient collection of shallow coastal seabed data in comparison to acoustic techniques. One important application of these sensors, given ALB seabed footprint size on the order to several meters in diameter for shallow waters, is the development of seabed classification maps and techniques to classify both benthic species and seabed sediment. The coastal seabed is a complex environment consisting of diverse habitats and, thus, necessitates classification methods which readily account for seabed class heterogeneity. Recent ALB classification studies have relied on classification techniques that assign each ALB shot to a single seabed class (i.e., hard classification) instead of allowing for assignment to multiple seabed classes which may be present in an illuminated ALB footprint (i.e., soft classification). 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 self-organizing 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 m~(2) collected from Quebec, Canada was used. Cross-validation of the SSC approach yields percentage cover accuracy of approximately 71.7percent with 16 seabed classes for a real ALB dataset, while dominant seabed class prediction based on hardening of percentage cover predictions yielded 66percent accuracy for 4 seabed classes.