A novel feature extraction model to enhance underwater image classification

Underwater images often suffer from scattering and color distortion because of underwater light transportation characteristics and water impurities. Presence of such factors make underwater image classification task very challenging. We propose a novel classification convolution autoencoder (CCAE), which can classify large size underwater images with promising accuracy. CCAE is designed as a hybrid network, which combines benefits of unsupervised convolution autoencoder to extract non-trivial features and a classifier, for better classification accuracy. In order to evaluate classification accuracy of proposed network, experiments are conducted on Fish4Knowledge dataset and underwater synsets of benchmark ImageNet dataset. Classification accuracy, precision, recall and f1-score results are compared with state-of-the-art deep convolutional neural network (CNN) methods. Results show that proposed system can accurately classify large-size underwater images with promising accuracy and outperforms state-of-the-art deep CNN methods. With the proposed network, we expect to advance underwater image classification research and its applications in many areas like ocean biology, sea exploration and aquatic robotics.