# Towards Highly Accurate Coral Texture Images Classification Using Deep Convolutional Neural Networks and Data Augmentation

arXiv: https://arxiv.org/abs/1804.00516 · HTML: https://mlsanity.com/paper/1804.00516/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Mar 2018.

## Built on by (most cited first, 11 of 11)

- [Point Label Aware Superpixels for Multi-species Segmentation of Underwater Imagery](https://mlsanity.com/paper/2202.13487/) (2022)
- [Ensembles of Vision Transformers as a New Paradigm for Automated Classification in Ecology](https://mlsanity.com/paper/2203.01726/) (2022)
- [Deep Learning methodology for the identification of wood species using high-resolution macroscopic images](https://mlsanity.com/paper/2406.11772/) (2024)
- [Deep learning for multi-label classification of coral conditions in the Indo-Pacific via underwater photogrammetry](https://mlsanity.com/paper/2403.05930/) (2024)
- [Multi-label classification for multi-temporal, multi-spatial coral reef condition monitoring using vision foundation model with adapter learning](https://mlsanity.com/paper/2503.23012/) (2025)
- [Habitat Classification from Ground-Level Imagery Using Deep Neural Networks](https://mlsanity.com/paper/2507.04017/) (2025)
- [A robust approach for deep neural networks in presence of label noise: relabelling and filtering instances during training](https://mlsanity.com/paper/2109.03748/) (2021)
- [Fuzzy Overclustering: Semi-Supervised Classification of Fuzzy Labels with Overclustering and Inverse Cross-Entropy](https://mlsanity.com/paper/2110.06630/) (2021)
- [COVID-19 Electrocardiograms Classification using CNN Models](https://mlsanity.com/paper/2112.08931/) (2021)
- [Pengembangan Model untuk Mendeteksi Kerusakan pada Terumbu Karang dengan Klasifikasi Citra](https://mlsanity.com/paper/2308.04337/) (2023)
- [YH-MINER: Multimodal Intelligent System for Natural Ecological Reef Metric Extraction](https://mlsanity.com/paper/2505.22250/) (2025)

## Abstract (arXiv)

The recognition of coral species based on underwater texture images pose a significant difficulty for machine learning algorithms, due to the three following challenges embedded in the nature of this data: 1) datasets do not include information about the global structure of the coral; 2) several species of coral have very similar characteristics; and 3) defining the spatial borders between classes is difficult as many corals tend to appear together in groups. For this reason, the classification of coral species has always required an aid from a domain expert. The objective of this paper is to develop an accurate classification model for coral texture images. Current datasets contain a large number of imbalanced classes, while the images are subject to inter-class variation. We have analyzed 1) several Convolutional Neural Network (CNN) architectures, 2) data augmentation techniques and 3) transfer learning. We have achieved the state-of-the art accuracies using different variations of ResNet on the two current coral texture datasets, EILAT and RSMAS.