Sonar Point Cloud Processing to Identify Sea Turtles by Pattern Analysis

Abstract

Abundant in coastal areas, sea turtles are affected by high-intensity acoustic anthropogenic sounds. In this article, we offer a pattern-analysis-based detection approach to serve as a warning system for the existence of nearby sea turtles. We focus on the challenge of overcoming the low signal-to-clutter ratio (SCR) caused by reverberations. Assuming that, owing to low SCR, target reflections within the point cloud are received in groups, our detector searches for patterns through clustering to identify possible “blobs” in the point cloud of reflections, and to classify them as either clutter or a target. Our unsupervised clustering is based on geometrical and spectral constraints over the blob's member relations. In turn, the classification of identified blobs as either a target or clutter is based on features extracted from the reflection pattern. To this end, assuming that reflections from a sea turtle are stable but include spectral diversity due to distortions within the turtle's body, we quantify the stability of the blob's members and the entropy of their reflection spectrum. We test our detector in both the modeled simulations, and at sea, for the detection of sea turtles released after rehabilitation. The results show robustness to highly fluctuating target intensity and ability to detect at low SCR.