Explicitly Integrated Multitask Learning in a Hybrid Network for Remote Sensing Road Extraction

The use of remote sensing images and supplemented by deep learning methods for road extraction has greatly accelerated work efficiency while still maintaining remarkable accuracy. In recent years, researchers have attempted to adopt a collaborative design of multiple interrelated tasks to further improve the performance of road extraction. Although the design expectations are met in most cases, there are still some problems: 1) The interaction among tasks is often only related to shared parameters or feature fusion with poor interpretability, and 2) There may be conflicts among tasks, which could lead to a decline in performance instead of an increase. To solve the above problems, this article proposes a novel hybrid network. This network adopts the integration of different architectures, each efficiently demonstrating its ability to extract global and local features, and achieving complementarity among them. Furthermore, we proposed the DBLFR module for guiding feature refinement from the perspective of the frequency domain. In addition, compared with implicit multitask interaction, the proposal of EMTL attempts to regard the multitask structure as a deeply supervised process and improve the network’s attention to the main tasks through explicit fusion. Our experiments on the DeepGlobe and CITY-OSM datasets verified the effectiveness of the proposed network and modules, and were significantly superior to the existing methods in most metrics.