Abstract
Coral reefs are the sub-aqueous calcium carbonate structures collected by the invertebrates known as corals. The charm and beauty of coral reefs attract tourists, and they play a vital role in preserving biodiversity, ceasing coastal erosion, and promoting business trade. However, they are declining because of over-exploitation, damaging fishery, marine pollution, and global climate changes. Also, coral reefs help treat human immune-deficiency virus (HIV), heart disease, and coastal erosion. The corals of Australia’s great barrier reef have started bleaching due to the ocean acidification, and global warming, which is an alarming threat to the earth’s ecosystem. Many techniques have been developed to address such issues. However, each method has a limitation due to the low resolution of images, diverse weather conditions, etc. In this paper, we propose a bag of features (BoF) based approach that can detect and localize the bleached corals before the safety measures are applied. The dataset contains images of bleached and unbleached corals, and various kernels are used to support the vector machine so that extracted features can be classified. The accuracy of handcrafted descriptors and deep convolutional neural networks is analyzed and provided in detail with comparison to the current method. Various handcrafted descriptors like local binary pattern, a histogram of an oriented gradient, locally encoded transform feature histogram, gray level co-occurrence matrix, and completed joint scale local binary pattern are used for feature extraction. Specific deep convolutional neural networks such as AlexNet, GoogLeNet, VGG-19, ResNet-50, Inception v3, and CoralNet are being used for feature extraction. From experimental analysis and results, the proposed technique outperforms in comparison to the current state-of-the-art methods. The proposed technique achieves 99.08% accuracy with a classification error of 0.92%. A novel bleached coral positioning algorithm is also proposed to locate bleached corals in the coral reef images.
Highlights
Coral reefs are one of the most important ecosystems on the planet because they help to maintain biodiversity and the life cycles of so many marine species
We propose a novel Bag of Features (BoF) technique integrated with support vector machine (SVM) to classify bleached and unbleached corals with high accuracy
bag of features (BoF) is a vector containing handcrafted features extracted with the help of Histogram of Oriented Gradient (HOG) and Local Binary Pattern (LBP) as well as spatial features extracted with AlexNet and CoralNet
Summary
Coral reefs are one of the most important ecosystems on the planet because they help to maintain biodiversity and the life cycles of so many marine species. In [8] hyper-spectral bottom index imagery is used for bottom-type classification in coral reef areas The drawback of this technique is the need for an enormous number of samples in the dataset for achieving higher accuracy. Motivated by the marine ecosystem’s protection, this manuscript proposes a deep learning influenced vision-based technique to detect and classify bleached and unbleached corals. Vector Machine (SVM), decision tree and k-nearest neighbor (kNN) are used as a classifier in combination with the corresponding deep learning influenced vision-based technique This manuscript’s main contribution is the classification of the bleached and unbleached corals using visual vocabulary which is combination of spatial, texture, and color features, followed by SVM with a linear kernel.
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