End-to-End DSM Fusion Networks for Semantic Segmentation in High-Resolution Aerial Images

Semantic segmentation in high-resolution aerial images is a fundamental research problem in remote sensing field for its wide range of applications. However, it is difficult to distinguish regions with similar spectral features using only multispectral data. Recent research studies have indicated that the introduction of multisource information can effectively improve the robustness of segmentation method. In this letter, we use digital surface models (DSMs) information as a complementary feature to further improve the semantic segmentation results. To this end, we propose a lightweight and simple DSM fusion (DSMF) branch structure module. Compared with the existing feature extraction structures, proposed DSMF module is simple and can be easily applied to other networks. In addition, we investigate four fusion strategies based on DSMF module to explore the optimal feature fusion strategy and four end-to-end DSMFNets are designed according to the corresponding strategies. We evaluate our models on International Society for Photogrammetry and Remote Sensing Vaihingen data set and all DSMFNets achieve promising results. In particular, DSMFNet-1 achieves an overall accuracy of 91.5% on the test data set.