Abstract
Among aquatic biota, corals provide shelter with sufficient nutrition to a wide variety of underwater life. However, a severe decline in the coral resources can be noted in the last decades due to global environmental changes causing marine pollution. Hence, it is of paramount importance to develop and deploy swift coral monitoring system to alleviate the destruction of corals. Performing semantic segmentation on underwater images is one of the most efficient methods for automatic investigation of corals. Firstly, to design a coral investigation system, RGB and spectral images of various types of corals in natural and artificial aquatic sites are collected. Based on single-channel images, a convolutional neural network (CNN) model, named DeeperLabC, is employed for the semantic segmentation of corals, which is a concise and modified deeperlab model with encoder-decoder architecture. Using ResNet34 as a skeleton network, the proposed model extracts coral features in the images and performs semantic segmentation. DeeperLabC achieved state-of-the-art coral segmentation with an overall mean intersection over union (IoU) value of 93.90%, and maximum F1-score of 97.10% which surpassed other existing benchmark neural networks for semantic segmentation. The class activation map (CAM) module also proved the excellent performance of the DeeperLabC model in binary classification among coral and non-coral bodies.
Highlights
Corals are a significant part of the marine ecosystem, with high primary productivity, providing habitation with ample nourishment for various underwater organisms [1,2]
The collected spectral image data were manually labelled with pixel-level categories for training Support Vector Machine (SVM), which resulted in a maximum accuracy of 89% for semantic segmentation of corals [18]
To further expand the applications of deep convolutional neural network (CNN) with enhanced performance accuracy for underwater monitoring, this paper presents the analysis of the morphological information by a novel DeeperLabC model that can automatically extract spatial features and perform semantic segmentation of the coral image
Summary
Corals are a significant part of the marine ecosystem, with high primary productivity, providing habitation with ample nourishment for various underwater organisms [1,2]. Satellite remote sensing, using spectral imaging, is commonly practiced for large-scale coral study [6,7]. The remote sensing spatial resolution is finite, and a particular spatial pixel in the image may have a large imaging area with varying types of corals. The pixel category is determined by comparing the morphological feature information of corals in the image [9]. This method has a high labor cost, and it is subjective to identification decisions by professionals. Both spectral imaging and RGB imaging techniques have been used to get morphological features. Various machine learning algorithms have been utilized for automatic detection and segmentation as discussed
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