MCNet: A Multi-scale and Cascade Network for Semantic Segmentation of Remote Sensing Images

High resolution remote sensing images that can show more detailed ground information play an important role in land classification. However, existing segmentation methods have the problems of insufficient use of multi-scale feature and semantic information. In this study, a multi-scale and cascade semantic segmentation network (MCNet) was proposed and tested on the Potsdam and Vaihingen datasets. (1) Multi-scale feature extraction module: using dilated convolution and a parallel structure to fully extract multi-scale feature information. (2) Cross-layer feature selection module: adaptively selecting features in different levels to avoid the loss of key features. (3) Multi-scale object guidance module: weighting the features at different scales to express the multi-scale ground objects. (4) Cascade structure in the decoder part: increasing the information flow and enhancing the decoding capability of the network. Results show that the proposed MCNet outperformed the baseline networks, achieving an average overall accuracy of 86.91% and 87.82% on the two datasets, respectively. In conclusion, the multi-scale and cascade semantic segmentation network can improve the accuracy of land cover classification by using remote sensing images.