Abstract
The use of very-high-resolution images to extract urban, suburban and rural roads has important application value. However, it is still a problem to effectively extract the road area occluded by roadside tree canopy or high-rise buildings to maintain the integrity of the extracted road area, the smoothness of the sideline and the connectivity of the road network. This paper proposes an innovative Cascaded Attention DenseUNet (CADUNet) semantic segmentation model by embedding two attention modules, such as global attention and core attention modules, in the DenseUNet framework. First, a set of cascaded global attention modules are introduced to obtain the contextual information of the road; secondly, a set of cascaded core attention modules are embedded to ensure that the road information is transmitted to the greatest extent among the dense blocks in the network, and further assist the global attention module in acquiring multi-scale road information, thereby improving the connectivity of the road network while restoring the integrity of the road area shaded by the tree canopy and high-rise buildings. Based on binary cross entropy, an adaptive loss function is proposed for network parameter tuning. Experiments on the Massachusetts road dataset and the DeepGlobe-CVPR 2018 road dataset show that this semantic segmentation model can effectively extract the road area shaded by tree canopy and improve the connectivity of the road network.
Highlights
Road information is of vital importance in the fields of urban and rural development [1], emergency and disaster relief [2], vehicle navigation [3] and geographic information systems [4]
In the Massachusetts dataset, the road occluding mainly comes from the tree canopy aside the rural and suburban roads, while the images occluded by urban roads are few
The Cascaded Attention DenseUNet (CADUNet) proposed in this paper has achieved good results on the blocked roads in the rural, suburban and urban areas
Summary
Road information is of vital importance in the fields of urban and rural development [1], emergency and disaster relief [2], vehicle navigation [3] and geographic information systems [4]. By improving the CNN architecture, Gao et al [17] proposed a deep residual convolutional neural network with post-processing operations, which showed good performance in extracting roads from complex backgrounds involving both urban and rural areas. Xin et al [40] proposed DenseUNet for road extraction in complex scenes based on DenseNet, considering its powerful capabilities on multi-level feature extraction and reuse These deep learning methods perform well, so that roads and buildings and other artificial surfaces are better classified. Oktay et al [51] proposed an attention module that learns weighted images from a high level to focus on the useful features and suppresses the irrelevant regions in the intermediate feature map, thereby improving the prediction performance.
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