Hierarchical Classification for Automated Image Annotation of Coral Reef Benthic Structures

TL;DR

This paper introduces a hierarchical classification method for automated coral reef image annotation, improving accuracy over flat classifiers and aligning better with ecological goals.

Contribution

It presents a novel hierarchical classification approach tailored for benthic image annotation, addressing limitations of existing flat classifiers.

Findings

Hierarchical classifier outperforms flat classifiers by ~2% in F1 scores.

Method aligns more closely with ecological classification objectives.

Effective across different training data sizes.

Abstract

Automated benthic image annotation is crucial to efficiently monitor and protect coral reefs against climate change. Current machine learning approaches fail to capture the hierarchical nature of benthic organisms covering reef substrata, i.e., coral taxonomic levels and health condition. To address this limitation, we propose to annotate benthic images using hierarchical classification. Experiments on a custom dataset from a Northeast Brazilian coral reef show that our approach outperforms flat classifiers, improving both F1 and hierarchical F1 scores by approximately 2\% across varying amounts of training data. In addition, this hierarchical method aligns more closely with ecological objectives.