Extracting roads from satellite images via enhancing road feature investigation in learning

It is a hot topic to extract road maps from satellite images. However, it is still very challenging with existing methods to achieve high-quality results, because the regions covered by satellite images are very large and the roads are slender, complex and only take up a small part of a satellite image, making it difficult to distinguish roads from the background in satellite images. In this article, we address this challenge by presenting two modules to more effectively learn road features, and so improving road extraction. The first module exploits the differences between the patches containing roads and the patches containing no road to exclude the background regions as many as possible, by which the small part containing roads can be more specifically investigated for improvement. The second module enhances feature alignment in decoding feature maps by using strip convolution in combination with the attention mechanism. These two modules can be easily integrated into the networks of existing learning methods for improvement. Experimental results show that our modules can help existing methods to achieve high-quality results, superior to the state-of-the-art methods. Two modules are developed to improve road feature detection, one for avoiding interferences from background regions, and the other for using strip convolution to enhance feature alignment. These two modules can be easily integrated with existing methods for promoting their effectiveness on road detection, as attested by the experimental results. image