# Attention Neural Network for Water Image Classification under IoT Environment
###### Abstract
However, both methods need high-contrast water images with distinct boundary and simple background for reliable classification results. Inspired by significant classification results achieved by typical Convolutional Neural Network (CNN) structure, quantity of trials on utilizing deep learning structures to comprehend water images have been made, where the core idea of such structures is to discover and apply the distinguished features for water image classification. Regarding water image as one typical category of natural scenes, [PERSON] et al. [9] explore d deep learning architectures to extract texture related feature for scene classification. Later, [PERSON] et al. [10] utilized CNN and dimension reduction technology to construct  
Figure 1: Workflow of water image based pollution monitoring system, where we can notice the task of the water image analysis model is to classify captured water images into two categories and 10 subcategories. a novel spectral-spatial feature for water and other natural scene classification. However, both methods simply implement deep learning structures to solve problems of scene classification without specific modifications. Based on the above discussion, we can notice that classification of water images is difficult with high intra-class and low inter-class properties, which can be defined as an ambiguous classification problem. Simply deploying neural networks cannot reach high accuracy in classification due to small number of training samples. Essentially, deep learning structures generally require big data to support semantic ambiguous classification. Expanding potentials on extracting representative features is proper to deal with insufficient training data. We thus propose to design a task-specific CNN for water image classification, which can achieve results with high precision and recall performance. Essentially, the major challenge for building a task-specific CNN for water image classification is to extract highly distinguished features for processing. With years of developing and comprehending the CNN structure, researchers tend to consider a convolutional layer compute feature map with structure of variant channel filters. Inside a feature map, each slice spatially encodes visual responses under the influence of channel filter. Essentially, such filter can be regarded as pattern recognizer, where low-level visual cues such as lines and texture can be recognized by lower-layer filters, while semantic meanings such as category are recognized by higher-layer ones. To prove such supposition, researchers stack layers to view output, where features are computed to form visual abstraction with a hierarchy structure. Such phenomenon coincides with hypothesis that features extracted from CNN own important channel-wise and multi-layer characteristics. Some features are useless for classifying water images. For example, the illumination channel can be useless for water image classification, since sensors are generally settled in the wild facing high variations in illumination. On the basis of irrelevant feature channels, water image analysis model could result in low effectiveness, since irrelevant feature channels bring noise for classification. We thus construct an attention neural network to pay attention to informative features. In this paper, we firstly construct channel-wise attention gate structure and then build attention neural network by hierarchically involving multi-layer attention gates. With task-specific structure, the proposed network resolves informative features for water image classification. There exist three main contributions in this work:  
* A context attention neural network for water image classification task is proposed, in which a task-specific attention model is proposed to encode channel-wise and multi-layer characteristics into features. * The proposed model introduces channel-wise attention gate structure and builds hierarchical attention structure by utilizing multiple attention gates in different layers.