# Attention Neural Network for Water Image Classification under IoT Environment
## 2 Related Work
### Water Image Classification  
Many studies have been applied to resolving problems of water pollution. Among them, one of the most important topics is to classify pollution types of water images. In fact, many related approaches have been developed to the benefit of accurately monitoring water information, including efforts to construct cloud-based monitoring systems [11; 12].  
Early, [PERSON] et al. [13] utilized a flip invariant shape detector for reflected contour detection. After locating reflected contours with edge features, their proposed method utilizes contour locationsto perform water image segmentation. Finally, their proposed method extracts features on sub-regions to perform detection task on water surface. Following their idea, [PERSON] et al. [14] detected water body by sky reflection, which computes similarity of intensity values to accurately locate water body. Later, [PERSON] et al. [15] proposed performing water detection following the principle of dynamic texture recognition guided segmentation, which adopts entropy to model and learn texture-related property of water images. [PERSON] et al. [16] fused multiple technologies to detect change of water surface. Their proposed approach could produce a sharpened multi-spectral image for classification, thus providing a high accuracy result. To sum up, traditional methods to resolve water pollution problem are composed of manual feature extraction and classifier construction steps, which are often complex, costly, and time-consuming.  
With the fast development of deep learning structures [17; 18; 19], researchers have applied more deep networks to perform classification tasks on water images. Inspired by CNN models to analyze close range photography, [PERSON] et al. [20] trained a CNN to perform classification tasks on input SAR image patches. After conducting comparative studies, they concluded that CNN is considerably better than the traditional classification methods, i.e., SVM, and has great potential to apply for SAR image interpretation. Regarding water surface as one important object category for classification, [PERSON] et al. [21] proposed vertex component analysis network (R-VCANet) to perform multiple objects classification by inputting hyperspectral images. R-VCANet is built on the basis of spatial and spectral characteristics of HSI data, which achieves higher accuracy even with limited size of training dataset.  
Following the idea of [PERSON] et al. [21], [PERSON] et al. [22] utilized a novel neural network to work on high-resolution remote sensing (HRRS) images for locations of urban water bodies. After segmenting inputting image into high-quality superpixels, their proposed method designs a task-specific CNN to extract semantical features of water surface, which is further applied to classify class label of superpixel: water or no-water pixel. The proposed method is similar to that of [PERSON] et al.'s [22] in classification goal. However, we perform ambiguous classification at image level rather than binary classification at pixel level, which is more challenging than the problem they considered. Based on development of CNN-based model for water interpretation and developing of cloud-edge computing [23; 24], [PERSON] et al. [25] developed a low-cost water surveillance system with cameras as main sensors, which could automatically predict water levels via a deep CNN structure.