Abstract
High-quality updates of road information play an important role in smart city planning, sustainable urban expansion, vehicle management, urban planning, traffic navigation, public health and other fields. However, due to interference from road geometry and texture noise, it is difficult to avoid the decline of automation while accurately extracting roads. Therefore, we propose a high-resolution optical satellite image lane-level road extraction method. First, from the perspective of template matching and considering road characteristics and relevant semantic relations, an adaptive correction model, an MLSOH (multi-scale line segment orientation histogram) descriptor, a sector descriptor, and a multiangle beamlet descriptor are proposed to solve the interference from geometry and texture noise in road template matching and tracking. Second, based on refined lane-level tracking, single-lane and double-lane road-tracking modes are designed to extract single-lane and double-lane roads, respectively. In this paper, Pleiades satellite and GF-2 images are selected to set up different scenarios for urban and rural areas. Experiments are carried out on the phenomena that restrict road extraction, such as tree occlusion, building shadow occlusion, road bending, and road boundary blurring. Compared with other methods, the proposed method not only ensures the accuracy of lane-level road extraction but also greatly improves the automation of road extraction.
Highlights
In recent years, the extraction of roads from remote-sensing images has gradually become the main way to update road information
Because the processing steps of this method have the basic sequence of segmentation, classification, and post-processing, when local deformation causes the segmentation unit to be over-segmented, the lack of an effective feedback mechanism will inevitably lead to misclassification of the segmentation unit, which reduces the accuracy of the road extraction
The reason for correcting the tracking point is that the tracking point determined by the sector descriptor may not be located at the center of the road, and the current tracking point is the basis for subsequent tracking
Summary
The extraction of roads from remote-sensing images has gradually become the main way to update road information. The most classical global matching method is the object-oriented method [2] In this method, roads are regarded as region units with spectral textural shape similarities that are segmented according to rules and extracted by classification and post-processing. Maboudi et al [4] applied multi-scale models combining color and shape information to segment images; classified the segmentation units based on structural, spectral, and textural characteristics; and applied the tensor voting method to connect road fractures. This type of method has been applied in eCognition software and has achieved good road extraction effects in regions with small spectral texture changes. Because the processing steps of this method have the basic sequence of segmentation, classification, and post-processing, when local deformation causes the segmentation unit to be over-segmented, the lack of an effective feedback mechanism will inevitably lead to misclassification of the segmentation unit, which reduces the accuracy of the road extraction
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