Predictive habitat models from AUV-based multibeam and optical imagery

Abstract

In AUV habitat mapping and exploration missions, a prior habitat map with associated uncertainty has the potential to guide the design of AUV deployments more effectively than a bathymetric map alone. We present and characterize an approach for learning predictive models of benthic habitats as a function of seabed terrain features. The models were learned by correlating limited-coverage high resolution imagery with full-coverage multibeam bathymetry data, both collected by an AUV at a site off the Tasman Peninsula in Tasmania, Australia. Correlations observed where these data overlapped were extrapolated to the much larger area covered by the multibeam survey. Accuracies of 0.69 − 0.78 were attained using a 10-fold cross-validation. A feature ranking analysis using bootstrap aggregation was also carried out revealing features were more informative at the larger scales of 5 × 5m 2 . Using bootstrap aggregation we learn probabilistic habitat maps for the site along with map of entropy that indicates areas of uncertainty. We discuss the implications for the planning of AUV missions and for the generation of adaptive trajectories aimed at improving map quality.

A new arc–chord ratio (ACR) rugosity index for quantifying three-dimensional landscape structural complexity

“…Since the rugosity-slope issue is embedded in the details of the standard SR equation, many ecological studies that use or suggest the SR method do not recognize the problem (Roberts and Ormond 1987;Ierodiaconou et al 2007;Wedding et al 2008). Some studies inadvertently exacerbate the issue by including both SR rugosity and slope as independent environmental parameters within the same analyses (Ahsan 2010;Henry et al 2013). The ACR rugosity index allows analysts to examine both parameters without confounding effects.…”

Towards Benthic Habitat 3D Mapping Using Machine Learning Algorithms and Structures from Motion Photogrammetry

“…However, most of these studies created 3D models for the overall area and then applied the classification process using either 3D models or some variables (e.g., slope and rugosity). Ahsan et al [37] proposed a predictive learning approach for benthic habitat mapping using 3D model features and seabed terrain features. These 3D model features included local binary, modified HSV histograms, and visual rugosity index.…”

“…However, the abovementioned approaches have several drawbacks: (1) producing 3D models for the overall area is a time consuming and labor intensive process, especially for mapping large study areas; (2) If the number of benthic habitats classes were to increase, the majority of these approaches would result in comparatively low classification accuracy; (3) integrating multibeam bathymetry, which is relatively expensive, with 3D mosaics to improve the results [37] would increase the process costs; and (4) the integrated bathymetric features were not sufficiently descriptive to classify these habitats. Accordingly, the proposed approach attempts to overcome these demerits by classifying high-resolution geo-referenced images using machine learning algorithms.…”