# Automatic Hierarchical Classification of Kelps using Deep Residual Features

## TL;DR

This paper introduces a hierarchical deep learning-based method for automatically classifying kelps in underwater images, outperforming traditional flat classification and hand-crafted features, with applications in monitoring kelp cover over time.

## Contribution

It proposes a novel hierarchical classification approach using deep residual features for kelp identification, improving accuracy over existing methods.

## Key findings

- Hierarchical classification achieves 90% accuracy, outperforming flat classification.
- Deep residual features outperform traditional CNN and hand-crafted features.
- Sibling hierarchical training outperforms inclusive hierarchical approach.

## Abstract

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.