Detecting roads from high-resolution photographs can serve forestry, agriculture, traffic and even military areas, and produce significant social and economic value. In this paper, we present a novel method that utilizes the flatness and the connectivity to detect the road in high-resolution aerial images. The method iterates the probable locations of the roads by using the flatness and connects the roads by using the connectivity. Firstly, we introduce a concept of ‘footprint’, which reveals the probable location and extension direction of a road. Given an initial footprint, we assess the flatness between locations to search the resulting footprint. By iterating and connecting the footprints, our approach produces a set of connected line segments that reflect the road to be detected. In addition, a footprints initialization algorithm is introduced to make our method totally automatic, and a road network pruning algorithm is designed to make the result clearer and more accurate. Tested under three high-resolution aerial photographs, our method achieved an accuracy of more than 80%. The algorithm is adapted for road detection and still linear target detection in high-resolution aerial photographs. Since the algorithm does not require artificial features or training data, it can be quickly deployed in application.
Extracting roads from remote sensing images is an important task in the remote sensing field. We propose an approach of designing a light encoder–decoder network for road extraction. We analyze the relationship between road features and the receptive field of encoder–decoder networks and point out that the light encoder–decoder network can be achieved by controlling its receptive field. Based on this, we design road extraction networks on the architecture of a general encoder–decoder network, according to data specifications. In addition, we propose an adaptive weighted binary cross-entropy loss function to solve the problem of data imbalance in the training process. We validate our approach on the Massachusetts roads dataset and the DeepGlobe road extraction dataset. The experimental results show that our method reduces 98% and 94% of the parameters, respectively, compared with general encoder–decoder networks, whereas the performance of road extraction keeps well. Our approach has fewer parameters and good performance, so it is easier to deploy on mobile platforms.
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