## 4 Experiment and Results
### Experimental Results and Analysis
#### 4.4.2 Results and Analysis  
In order to analyze the performance of different classification algorithms, the proposed remote sensing image classification network, U-Net network and SegNet network are used for training. After obtaining the optimal model, the testing images are predicted. In the testing images, the proportion of each object, i.e., vegetation, building, water-body and road, is 0.50, 0.36, 0.08 and 0.06, respectively. Figure 10 shows part of the classification results for the testing images. Figure 10 shows an original color remote sensing image. Figure 10 represents the groundtruth, including vegetation, building, water-body, road, and others. Figure 10,d show the experimental results obtained by the U-Net network and the SegNet network, respectively. Figure 10 shows the classification results of the proposed method. As can be seen from Figure 10, the U-Net and SegNet networks are prone to misclassification in areas where the ground objects are close, and the classification effect is not ideal at the edge of the object. In addition, these methods have relatively poor classification results for the samples a with small number, i.e., road. However, the proposed network has a better classification performance. MFCSNet can accurately classify different ground objects. More accurate edges classification of ground objects can be obtained by the proposed method. It proves that the improved max-pooling indices decoding structure can better preserve the object edge and its location information, and the multi-scale feature coding structure can make the features more discriminative, which can achieve better classification results. In addition, for the segmentation performance of road, MFCSNet can classify more road regions and water-body regions. It proves that the proposed algorithm has better classification effect on the smaller number of categories than U-Net and SegNet networks. It also demonstrates that the cost-sensitive loss function, data enhancement and sample equalization processing are effective for sample imbalance problems and improving the precision of smaller sample category. In order to quantitatively analyze the classification performance of the proposed algorithm. We calculate the confusion matrix of multi-objective classification experimental results, as shown in Table 1.  
As shown in Table 1, it proves that the proposed classification network has achieved good results. The correct identification number of each type of sample is relatively high. Building and vegetation are prone to misclassification. Considering Figure 10, it has been observed that the building and the vegetation around the building are easily confused. Besides, many pixels of road have been misclassified to building and vegetation. In order to quantitatively analyze the performance of different classification algorithms. This paper reports the precision, Kappa coefficient and overall accuracy (OA) of the various experimental results, as shown in Table 2. The optimal classification model of U-Net, SegNet and the proposed network are trained, and then all testing images are tested. It can be seen from Table 2 that the overall accuracy of the proposed network reaches 94.46%, which is higher than 91.01% and 92.54% of the U-Net network and the SegNet network. It shows that the image features can be extracted more efficiently by the multi-scale feature encoding and decoding structure, which can get a better classification performance. The road classification rate of the proposed method is significantly higher than that of SegNet and U-Net. This is due to the cost-sensitive loss function proposed in this paper, which increases the penalty for road error classification and makes the network model converge toward a more reasonable direction. For the precision of each class, the proposed method achieved at least 0.77%, 1.93%, 4.06% and 7.15% higher than other methods with regard to vegetation, building, water and road, which has obvious advantages.