Automated processing of coral reef benthic images

Abstract

We describe an automated computer algorithm for the classification of coral reef benthic organisms and substrates sampled using a typical photographic quadrat survey. The technique compares subsections of a quadrat sample image (blocks) to a library of identified species blocks and computes a distance or probability of identification in a multidimensional hypervolume of discrimination metrics. The discrimination metrics include texture (calculated from a radial sampling of a two-dimensional discrete cosine transform) and three channels of a normalized color space. A standard multivariate classification technique based on the Mahalanobis distance was unsuccessful in discriminating substrata because of the large morphological variation inherent in reef organisms. An alternative classification scheme based on an exhaustive search through an organism reference library yielded classification maps comparable to those obtained by manual analysis.