Abstract
Efficient and accurate road extraction from remote sensing imagery is important for applications related to navigation and Geographic Information System updating. Existing data-driven methods based on semantic segmentation recognize roads from images pixel by pixel, which generally uses only local spatial information and causes issues of discontinuous extraction and jagged boundary recognition. To address these problems, we propose a cascaded attention-enhanced architecture to extract boundary-refined roads from remote sensing images. Our proposed architecture uses spatial attention residual blocks on multi-scale features to capture long-distance relations and introduce channel attention layers to optimize the multi-scale features fusion. Furthermore, a lightweight encoder-decoder network is connected to adaptively optimize the boundaries of the extracted roads. Our experiments showed that the proposed method outperformed existing methods and achieved state-of-the-art results on the Massachusetts dataset. In addition, our method achieved competitive results on more recent benchmark datasets, e.g., the DeepGlobe and the Huawei Cloud road extraction challenge.
Highlights
Published: 29 December 2021With the rapid development of earth observation technology, large-scale and highresolution remote sensing imagery has become the most important data source for object extraction
Because the high-resolution features extracted from shallow convolutional layers contain spatial details that are vital for small object recognition, such as narrow roads, it may introduce noise for the insufficiently represent features extracted through a shallow convolutional layer
We introduced the channel attention mechanism to realize the adaptive fusion of features at different scales, and optimizes the spatial detail and semantic information of features, thereby enhancing the feature for road representation
Summary
Published: 29 December 2021With the rapid development of earth observation technology, large-scale and highresolution remote sensing imagery has become the most important data source for object extraction. Despite existing extensive research about automatic road extraction, the accurate and efficient extraction of roads from remote sensing images for GIS applications is still a great challenge. This is partially due to the complexity between roads and backgrounds and partially due to the variation in the width of roads and in the spatial resolution of images [1,2]. Deep learning-based methods can automatically learn and extract representative and distinctive features from a large number of training samples They have been widely applied in remote sensing because they achieve higher performance than traditional road extraction methods [3,4,5]. The encoder module extracts multi-scale features from the input images, the decoder module interprets and upsamples the features end to end for Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations
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