Abstract
Road semantic segmentation is unique and difficult. Road extraction from remote sensing imagery often produce fragmented road segments leading to road network disconnection due to the occlusion of trees, buildings, shadows, cloud, etc. In this paper, we propose a novel fusion network (FuNet) with fusion of remote sensing imagery and location data, which plays an important role of location data in road connectivity reasoning. A universal iteration reinforcement (IteR) module is embedded into FuNet to enhance the ability of network learning. We designed the IteR formula to repeatedly integrate original information and prediction information and designed the reinforcement loss function to control the accuracy of road prediction output. Another contribution of this paper is the use of histogram equalization data pre-processing to enhance image contrast and improve the accuracy by nearly 1%. We take the excellent D-LinkNet as the backbone network, designing experiments based on the open dataset. The experiment result shows that our method improves over the compared advanced road extraction methods, which not only increases the accuracy of road extraction, but also improves the road topological connectivity.
Highlights
Road extraction is widely used in many urban applications such as road map updating, geographic information updating, car navigations, geometric correction of urban remote sensing image, etc. [1,2,3]
The mean intersection over union (mIoU) (63.31%) of the proposed model is 1.38 higher than HsgNet based on attention mechanism and 2.41 higher than D-LinkNet, which was the first place in the road extraction competition in 2018; (2) compared with the model without GPS data, the accuracy of all models with GPS location data is obviously improved; (3) the road connectivity is effectively improved by the proposed model, but the results are not as good as the Road Connectivity model [62] focusing on road connectivity
This paper mainly aims to improve the accuracy of road extraction and the road connectivity
Summary
Road extraction is widely used in many urban applications such as road map updating, geographic information updating, car navigations, geometric correction of urban remote sensing image, etc. [1,2,3]. Road region segmentation based on remote sensing images [4] has its unique and difficult characteristics, which are manifested in Figure 1: (1) The road is long and narrow, it occupies a small proportion of the whole image, and often covers the whole image; (2) the topological connectivity relationship is complex, especially in the road intersection; (3) the geometric features are similar to the water system and railway; (4) the texture features are easy to be confused with the surrounding background environment; (5) the extracted roads are not connected due to the occlusion of trees, shadows, buildings, etc.
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