Tsdlinknet: D-Linknet with Transformer and scSE Module for High Resolution Remote Sensing Images Road Extraction

Road extraction for the high-resolution remote sensing images has important applications in urban planning, traffic management, geographic information systems(GIS) and other fields. However, The impact of the environment such as pedestrians and farms causes discontinuity and incompleteness in extraction results. In this paper, We present D-Linknet with transformer and Concurrent Spatial and Channel Squeeze and Channel Excitation(scSE) module(TSDlinknet) which is built with D-Linknet architecture. Firstly, we use transformer encoders in the central part instead of D-Block to handle long-range dependencies efficiently. Secondly, scSE module is added after each convolutional layer in the decoding stage to refine spatial information and channel information of the feature map. Experimental results on CHN6-CUG road dataset demonstrate that our method outperforms D-Linknet101 by 5.6% IOU higher, in addition, our extraction results are more continuous and complete.