Hierarchical Classification for Automated Image Annotation of Coral Reef Benthic Structures
This work addresses the need for efficient coral reef monitoring against climate change, but it is incremental as it builds on existing classification methods with a hierarchical approach.
The paper tackled the problem of automated benthic image annotation for coral reef monitoring by proposing hierarchical classification to capture the natural hierarchy of benthic organisms, resulting in improvements of about 2% in F1 and hierarchical F1 scores compared to flat classifiers.
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.