A novel feature descriptor based coral image classification using extreme learning machine with ameliorated chimp optimization algorithm

Coral reefs are one of the most biodiverse and productive ecosystems on earth, which acts as a habitat and provides food to a wide range of organisms. The coral reefs protect the coasts from storms, waves, and erosion. Unfortunately, this ecosystem is under threat by various natural and manmade factors, including pollution, ocean acidification, sedimentation, and climate change. The marine ecosystems need to be monitored regularly in order to minimize the aforementioned threats. Hence, an automated classification system is necessary for the classi-fication of coral images. In this paper, a novel method called "Systematic Local Directional Encoded Derivative Binary Pattern " (SLDEDBP) is proposed to extract the coral reef feature values. In this descriptor, the feature values are derived using two phases: Local Directional Derivative Binary Pattern (LDDBP) and Systematic Local Discriminate Binary pattern (SLDBP). In the LDDBP phase, the eight directional kirsch mask is employed in each 3 x 3 neighbourhood window to extract the directional edge response of an image. Then, in the SLDBP phase, the local neighbourhood pixel values are divided into four non-overlapping groups of Z, M, TZ (Tilted Z), and TM (Tilted M) to extract unique features values by considering the centre pixel value in a 3 x 3 neighbourhood window. Finally, the derived feature values of LDDBP and SLDBP are concatenated to procedure final discrim-inate feature values. The extracted feature values of coral images are applied to a hybridized method called Ameliorated Chimp Optimization Algorithm with Extreme Learning Machine Classifier (AChOA-ELM) to opti-mize the relative weight of the input and biases of neural networks. This avoids overfitting and makes the model more generalized and robust. The proposed AChOA-ELM method performance is evaluated using different da-tabases of coral and standard images. The reliability of the proposed model has been evaluated utilizing the model performance metrics, accuracy (ACC), true positive rate (TPR), true negative rate (TNR), false-positive rate (FPR), precision (PRC), positive predictive value (PPV), and F1 score. The findings reveal that the pro-posed hybrid AChOA-ELM achieves greater classification accuracy with a faster coverage rate than other existing methods. In the presence of the image data augmentation technique, the EILAT2 dataset achieves the highest classification accuracy of 99%, whereas MLC dataset achieves the lowest classification accuracy of 94.86%.