# Attention Neural Network for Water Image Classification under IoT Environment
## 4 Results and Discussion
### Performance Analysis
By comparing between the proposed WA and WoA methods, we can notice promotion on most of measurements is high due to the implementation of channel-wise attention module, which proves the effectiveness of utilizing context information for enhancement of the CNN feature map. The reason to achieve the same value in precision, recall, and F-score on clean water subclass lies in the fact that the dataset is unbalanced in numbers, where the number of clean water images is rather low and increasing measurement values with a small dataset is extremely difficult. For comparative studies, [PERSON] et al. [8] achieved poor results, since their spatiotemporal descriptor is initially proposed for water surface detection. Their method has a high standard for quality of input images with high-contrast water and distinct boundary. In our adopted dataset, there exist many irregular objects, such as rubbish, dead animals, etc., in polluted images, thus leading to poor classification results. Meanwhile, [PERSON] et al. [33] utilized HSV and Fourier spectrum to construct representative features, which overcomes the shortage of visual irregular shapes by frequency domain analyses. Therefore, their proposed method is much better than that of [PERSON] et al. [8] in classification performance. However, patterns of water images cannot be successfully detected by utilizing a manual feature. In Figure 5c, we can notice misclassification of dead animal as rubbish is 0.89, while correctness to classify rubbish is 0.87. After analyzing, we believe such phenomenon can be explained by the fact that the manually-designed spectrum feature proposed by [PERSON] et al. [33] is highly effective for recognizing rubbish other than dead animals. Figure 6: Classification confusion matrix for clean water images: (**a**) the matrix achieved by the proposed method (WA); (**b**) the matrix achieved by the proposed method (WoA); (**c**) the matrix achieved by [PERSON] et al. [33]; and (**d**) the matrix achieved by [PERSON] et al. [8]. Essentially, low inter-class and high intra-class variations make the task of water image classification rather difficult, where one proper example is to recognize between rubbish and dead animals subclasses. Involving the ability to focus on informative visual cues, the proposed network greatly improves performance in clean water image classification, which could be proved by the confusion matrix in Figure 6. This is due to high visual resemblance of clean water images, where we should not only use high-level features, but also request assistance in classification from low-level features. However, noise could be brought by much information from low-level features to affect overall results. Therefore, a slight decrease in recall for polluted category can be viewed in Figure 7.  
Facing difficulties brought by ambiguous classification, we propose to encode context information including channel-wise and multi-layer properties for feature map enhancement, which greatly improves representation ability of generated feature map to act as qualified pattern detectors. However, misclassification still occurs in results achieved by the proposed network. For example, in Figure 5a, we can observe the 18%, 29%, and 6% of oil polluted samples are misclassified as clean river, industrial pollution, and rubbish, respectively. According to the analysis on these misclassified  
Figure 7: Classification confusion matrix for polluted water images: (**a**) the matrix achieved by the proposed method (WA); (**b**) the matrix achieved by the proposed method (WoA); (**c**) the matrix achieved by [PERSON] et al. [33]; and (**d**) the matrix achieved by [PERSON] et al. [8]. images, we find small inter-class variations among samples promotes high misclassification rate. Despite coupling with attention scheme to boost performance, water image classification as an ambiguous classification is still difficult to be completely resolved.