https://scholars.lib.ntu.edu.tw/handle/123456789/496984
標題: | Accurate Road Detection from Satellite Images Using Modified U-net | 作者: | Constantin, A. Lee, Y.-C. JIAN-JIUN DING |
關鍵字: | convolutional neural network (CNN); Deep learning; road extraction; satellite images | 公開日期: | 2019 | 起(迄)頁: | 423-426 | 來源出版物: | 2018 IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2018 | 摘要: | In this paper, we present an accurate neural network algorithm to detect roads in satellite images. Based on convolutional neural networks, from a 6-channel image, this model is able to transfer the road structure to the output using both the U-net and the atrous convolution architecture. To train this model, we introduce a new combination of existing loss functions including the binary cross-entropy and the Jaccard distance to avoid false positive detection and increase binary classification accuracy. In terms of precision, recall, the F-score and accuracy, experiments carried out using the Massachusetts roads dataset, provide better results than state-of-The-Art road extraction models. © 2018 IEEE. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062234911&doi=10.1109%2fAPCCAS.2018.8605652&partnerID=40&md5=b6133dfdbeed4efb14304aecff22558a | DOI: | 10.1109/APCCAS.2018.8605652 | SDG/關鍵字: | Convolution; Deep learning; Extraction; Neural networks; Roads and streets; Satellites; Binary classification; Convolutional neural network; False positive detection; Jaccard distance; Neural network algorithm; Road extraction; Satellite images; State of the art; Feature extraction |
顯示於: | 電機工程學系 |
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