Hierarchical Classification of Kelps utilizing Deep Residual Features

Across the globe, remote image data is rapidly being collected for the assessment of benthic communities from shallow to extremely deep waters on continental slopes to the abyssal seas. Exploiting this data is presently limited by the time it takes for experts to identify organisms found in these images. With this limitation in mind, a large effort has been made globally to introduce automation and machine learning algorithms to accelerate both classification and assessment of marine benthic biota. One major issue lies with organisms that move with swell and currents, like kelps. This paper presents an automatic hierarchical classification method to classify kelps from images collected by autonomous underwater vehicles. The proposed kelp classification approach exploits learned image representations extracted from deep residual networks. These powerful and generic features outperform the traditional off-the-shelf CNN features, which have already shown superior performance over the conventional hand-crafted features. Experiments also demonstrate that the hierarchical classification method outperforms the common parallel multi-class classifications by a significant margin. Experimental results are provided to illustrate the efficient applicability of the proposed method to study the change in kelp cover over time for annually repeated AUV surveys.