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 (local binary
classification as opposed to the conventional flat classification) to classify
kelps in images collected by autonomous underwater vehicles. The proposed kelp
classification approach exploits learned feature representations extracted from
deep residual networks. We show that these generic features outperform the
traditional off-the-shelf CNN features and the conventional hand-crafted
features. Experiments also demonstrate that the hierarchical classification
method outperforms the traditional parallel multi-class classifications by a
significant margin (90.0% vs 57.6% and 77.2% vs 59.0%) on Benthoz15 and
Rottnest datasets respectively. Furthermore, we compare different hierarchical
classification approaches and experimentally show that the sibling hierarchical
training approach outperforms the inclusive hierarchical approach by a
significant margin. We also report an application of our proposed method to
study the change in kelp cover over time for annually repeated AUV surveys.Comment: MDPI Sensor