Abstract
Recently, with the enhancement in the field of remote sensing and computation techniques, road detection from satellite images is getting possible. In these days, precise extraction of the lane from satellite images has become one of the major important fields of research in both remote sensing and transportation. The road network performs an imperative role in the traffic system, urban planning, route planning, and self-driving. In this paper, technique for road segmentation from the satellite images has been introduced. In the proposed method, a custom deep neural network (DNN) has been used for the detection of the road from satellite images. We have used a simple and custom neural network which is computationally faster and as accurate as a traditional deep neural network like Inception, YOLO, and ResNet-50 for road detection in the satellite images. In the initial stage, images are preprocessed with the help of OpenCV and morphology. We have annotated each pixel value as 0 for non-lane pixels and 1 for lane pixels. With this annotated data, we have trained our custom DNN model. The road region is denoted by white pixels, and black pixel denotes a non-road region. In the final result, the noise removal technique is used to remove distorted white pixels to improve the accuracy further.
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Trivedi, H., Sheth, D., Barot, R., Shah, R. (2021). Road Segmentation from Satellite Images Using Custom DNN. In: Singh, P.K., Wierzchoń, S.T., Tanwar, S., Ganzha, M., Rodrigues, J.J.P.C. (eds) Proceedings of Second International Conference on Computing, Communications, and Cyber-Security. Lecture Notes in Networks and Systems, vol 203. Springer, Singapore. https://doi.org/10.1007/978-981-16-0733-2_66
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DOI: https://doi.org/10.1007/978-981-16-0733-2_66
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