Hierarchically Channel-Wise Attention Model for Clean and Polluted Water Images Classification

Abstract

Water image classification is challenging because clean water images of ocean or river share almost the same properties with images of polluted water. Inspired by the significant power of Convolutional Neural Network (CNN) in extracting various features for classification, we intend to utilize CNN to classify clean and polluted water images based on quantity of channel-wise and multi-layer CNN-extracted features. Since not all features are informative for water image classification, a dynamic feature attention scheme that utilize the properties of channel-wise and multi-layer is necessary to achieve robust and accurate results. In this paper, we propose a novel hierarchically channel-wise attention model for clean and polluted water images classification. The proposed model dynamically modulates context with multi-layer feature maps in a local and global sense, constructing a representative combination of features to boost classification performance. Experimental results on a latest water image dataset (reporting 71.2% in accuracy) with several comparative methods demonstrate the effectiveness and robustness of the proposed model incorporating CNN for water image classification.