An Object Based Approach for Submarine Canyon Identification from Surface Networks

Abstract

GIScience 2016 Short Paper Proceedings An Object Based Approach for Submarine Canyon Identification from Surface Networks Andres Cortes Murcia 1,2 , Eric Guilbert 1,2 , Mir Abolfazl Mostafavi 1,2 Dept. of Geomatics Sciences, Universite Laval, Quebec, G1V 0A6 (QC) Canada Center for research in geomatics, Universite Laval, Quebec, G1V 0A6 (QC) Canada Email: andres.cortes-murcia.1@ulaval.ca {eric.guilbert; mir-abolfazl.mostafavi}@scg.ulaval.ca Abstract In this paper we propose using surface networks to identify submarine canyons from bathymetric data. Identification is done in two steps. First, thalweg lines that fit the canyon definition are extracted; second, the floor around each thalweg is measured to separate steep narrow canyons from broader channels. Results are validated against a classification provided by geomorphologists. 1. Introduction Submarine canyons are relevant features for geomorphologists because they can explain the origin and evolution of marine landscape. Although there is a common understanding of what a canyon is, its description is often vague and its definition is not applicable for automatic classification. This issue leads to define parameters with arbitrary values that are difficult to establish. Traditional methods require image segmentation and classification. These approaches compute discrete local descriptors such as the curvature and depend on threshold parameters chosen by the user. Furtermore, image classification can omit global photo-interpretative characteristics. In general, photo-interpreters identify canyons by their overall shape (narrow, elongated, steep slopes) and position (running across the continental slope in a straight line), observed around salient thalwegs. This paper proposes an approach where thalwegs are extracted from a terrain surface network and canyons are built around them. We move away from pixel classification to an object-oriented approach built on a topological structure. The surface network is a graph where critical points such as pits and peaks are connected by ridges and thalwegs. Its extraction does not require any parameter. Relevant thalwegs are selected by simplifying the surface network. Simplification parameters are not set locally at pixel level but at the structure level. The valley floor is computed around each thalweg and used to classify canyons and other channels. The method is illustrated on a triangulated irregular network generated from multibeam sounding data from the St-Lawrence estuary (Canada). Results were validated against a manual classification performed by geomorphologists (Normandeau et a.l 2015). 2. Surface Network Construction A surface network is a topological graph. Its nodes are pits (local minima), peaks (local maxima) and saddles (points being a local maximum in one direction and a local minimum in another direction). Saddles are connected to peaks by ridges and to pits by thalwegs (Figure 1). The integrity of the surface network is guaranteed by several topological constraints.