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

Ramu Narayanan; Gunho Sohn; Heungsik B. Kim; John R. Miller

doi:10.1117/1.3595267

1 January 2011 Soft classification of mixed seabed objects based on fuzzy clustering analysis using airborne LIDAR bathymetry data

Ramu Narayanan, Gunho Sohn, Heungsik B. Kim, John R. Miller

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 5, Issue 1, 053534 (January 2011). https://doi.org/10.1117/1.3595267

Abstract

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 approximately20000 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 coverpredictions yielded 66% accuracy for 4 seabed classes.

©(2011) Society of Photo-Optical Instrumentation Engineers (SPIE)

Citation Download Citation

Ramu Narayanan, Gunho Sohn, Heungsik B. Kim, and John R. Miller "Soft classification of mixed seabed objects based on fuzzy clustering analysis using airborne LIDAR bathymetry data," Journal of Applied Remote Sensing 5(1), 053534 (1 January 2011). https://doi.org/10.1117/1.3595267

Published: 1 January 2011

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available