# Fully Convolutional Neural Network with Augmented
## 3 Experimental Results, Analysis and Discussion
### Experiment and Analysis
#### 3.4.3 Experiments on HRRS Dataset  
We compared the state-of arts in Table 3 from two aspects of accuracy and mean IoU (mIoU). According to the HRRS data training, the accuracy of the A-ASPP is almost the same as that of the method with FC fusion path, and the overall accuracy of the second method is improved by at least 10% when compared with the baseline method. From the IoU indicator, the segmentation effect improved by FC and A-ASPP is different. The effect of FC is relatively average in each category. The A-ASPP is more effective in improving the size of small-sized objects, such as road water. Because FC provides more underlying information flow through hopping connections, the A-ASPP structure uses deeper and more detailed deep information to enhance the network's expressive ability. The two methods complement each other, so the effect is improved when they work together. The results suggest that the improved model can obviously enhance the feature extraction ability of the model for RS images and thus improve the segmentation accuracy. The method with both A-ASPP and FC fusion path achieves the highest accuracy when \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**FC-Block** & **Road** & **Water** & **Architecture** & **Plant** & **Background** & **mIoU** & **Acc** \\ \hline baseline & 40.4 & 47.5 & 83.6 & 74.2 & 75.4 & 64.2 & 77.2 \\ \hline block1 & 41.1 & 46.9 & 83.0 & 74.4 & 76.9 & 64.5 & 79.0 \\ block2 & 43.2 & 48.4 & 84.3 & 74.2 & 75.4 & 65.1 & 83.2 \\ block3 & 43.7 & 49.5 & 83.1 & 75.3 & 76.8 & 65.7 & 85.6 \\ block4 & **44.2** & **49.9** & **83.9** & **75.1** & **77.2** & **66.1** & **87.1** \\ \hline PROD & 43.4 & 49.1 & 83.6 & 74.9 & 75.8 & 65.4 & 85.7 \\ SUM & **44.2** & **49.9** & **83.9** & **75.1** & **77.2** & **66.1** & **87.1** \\ MAX & 42.9 & 48.8 & 83.4 & 74.8 & 76.9 & 65.4 & 84.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Ablation studies on FC fusion path on HRRS data in terms of mean Intersection of Union (IoU) (mIou) and Acc. The best model performance is shown in bold. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Method** & **Road** & **Water** & **Architecture** & **Plant** & **Background** & **mIoU** & **Acc** \\ \hline FCN-8s & 23.4 & 37.5 & 53.2 & 52.2 & 55.1 & 44.3 & 61.4 \\ Unet & 36.1 & 41.9 & 66.1 & 62.1 & 57.2 & 52.7 & 67.5 \\ SegNet & 39.2 & 47.8 & 70.1 & 64.4 & 65.3 & 57.4 & 71.7 \\ PSPNet & 42.4 & 50.1 & 72.6 & 73.2 & 72.8 & 62.2 & 74.5 \\ RefineNet & 41.3 & 49.7 & 76.7 & 72.4 & 73.5 & 62.7 & 76.2 \\ DeepLabv3 & 40.4 & 47.5 & 83.6 & 74.2 & 75.4 & 64.2 & 77.2 \\ \hline A-ASPP & 50.7 & 53.7 & 84.4 & 75.3 & 76.1 & 68.0 & 88.9 \\ ASPP + FC & 44.2 & 49.9 & 83.9 & 75.1 & 77.2 & 66.1 & 87.1 \\ A-ASPP + FC & **52.5** & **54.2** & **84.9** & **76.1** & **77.8** & **69.1** & **91.4** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results on HRRS data in terms of mean IoU (mIou) and Acc. The best model performance is shown in bold. compared with the A-ASPP and FC fusion methods. Our method is further improved, as the small object segmentation effect is obviously improved, the road IoU is 14% higher than achieved using DeepLab v3, the water IoU is increased by 7%, and the overall effect is the best, the precision reaches 91.4%, mIoU reached 69.1%. By pre-training on ImageNet, we scored significantly better than DeepLab v3 with the same settings. Therefore, both improvements complement each other and enhance the prediction result. The visual results are shown in Figure 5.  
From Figure 5, we can see that the results of the baseline DeepLab v3 network model [28] are very rough, the edges are fuzzy, the labeling of roads and water is obviously wrong, and the visual effect is the worst. With the addition of the FC fusion path and the improvement of the ASPP structure, the edges of objects became clearer, although there are still some miss-classifications. When two improved models (A-ASPP and FC fusion path) are added at the same time, the visual effect is the best.