## 5 Conclusions  
In this paper, we proposed a novel MFCSNet network for remote sensing image semantic segmentation. MFCSNet uses multi-scale deep features fusion and cost-sensitive loss function to make the segmentation network more effective. For the features extraction and fusion, MFCSNet provides a novel framework with multi-scale feature coding structure and max-pooling indices decoding structure. Instead of using FCN and SegNet, MFCSNet combines the corresponding down-sampling feature maps during up-sampling process to extract the low-level and high-level fusion features. Different U-Net framework, max-pooling indices decoding is used for up-sampling process, which can retain objects location information. This up-sampling structure can improve the recognition rate of the object edge. In MFCSNet, the batch normalization acceleration layer can reduce the sensitivity of the learning rate and improve the network convergence speed, which can make the network parameters optimize easily. To improve the classification performance on the category with a small number of samples, we design a cost-sensitive loss function by increasing the penalty for the misclassification of the object. For the MFCSNet model training, data enhancement and sample equalization processing are performed on the training set, which can suppress network overfitting and improve the generalization ability of the network. Compared with other state-of-the-art methods, i.e., U-Net and SegNet, the classification of all the objects with the proposed method has better classification accuracy. The precision of each category of MFCSNet has achieved the highest value. The OA and Kappa coefficient of MFCSNet is 94.46% and 0.9113, respectively, which are at least 1.92% and 0.0303 higher than U-Net and SegNet. Besides, the integrity of each object segmented by MFCSNet is obviously better than the others, especially road. Thus, the proposed MFCSNet can achieve promising performance in overall classification performance, feature edge recognition, consistency of prediction results, and so on. Although our algorithm achieves promising semantic segmentation accuracy for the Jage data set, there are some drawbacks and interesting ideas which can be further explored to extend the research reported in this paper. The segmentation of MFCSNet is based on the single pixel, which makes the contour information of the object not complete and the associated information between the objects lost. Thus, for the proposed method, the segmentation of some small roads is not complete, and there are some noisy pixels. Besides, in the test phase of this method, the large-size image needs to be divided into small blocks for testing. The test results are spliced to obtain the segmentation result of the original image. This can result in unevenness in the splice regions. In the future, the higher-order potentials, which constructed by different constraints based on the object structure information, the adjacency of pixels and topologies of different levels of segmentation regions will be introduced to construct the CRF model to optimize the rendering of the segmentation. In addition, some post-processing methods, such as morphological filtering, will also be used in the optimization of segmentation results. Conceptualization, E.W., Y.L. and Q.Z. ; methodology, E.W., Y.L. and Y.J. ; software, M.R. and Q.Z. ; validation, Y.J., Y.Y. and E.W. ; data curation, Y.L. and M.R. ; writing--original draft preparation, Y.L., Q.Z. and M.R. ; writing--review and editing, Y.L., M.R and J.Y. ; visualization, J.Y. and E.W. ; supervision, Y.J. and E.W. ; project administration, Y.J. and J.Y. ; funding acquisition, Y.J. This research was funded by the Natural Science Young Foundation of Hebei Provincial Department of Education QN2017324.The APC was funded by YJ. The authors declare no conflict of interest.