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- W2966970137 abstract "Road is an important semantic region in remote sensing imagery and plays an important role in many applications. Deep learning has obtained a great success in image classification, since it can directly learn from labeled training samples and extract different level image features to encode the input image. In this paper, we propose a multi-scale convolutional neural network (MSCNN) for extracting road from high-resolution remote sensing image, in which road detection can be seen as a regional classification. This core trainable detection engine consists of an encoder-decoder network and a fusion model. Firstly, image was encoded as a feature representation with several stacked convolutional layers. And then guided by the different scale road training data, the pre-trained decoder networks output a series of classification maps. Finally, we investigate the fusion model utilizing different scale classification maps and obtain a final road decision map. To validate the performance of the proposed method, we test our MSCNN based method and other state-of-the-art approaches on two challenging datasets of high-resolution images. Experiments show our method gets the best results both in quantitative and qualitative evaluation." @default.
- W2966970137 created "2019-08-22" @default.
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- W2966970137 date "2019-01-01" @default.
- W2966970137 modified "2023-10-03" @default.
- W2966970137 title "Multi-scale Convolutional Neural Network for Road Extraction in Remote Sensing Imagery" @default.
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- W2966970137 doi "https://doi.org/10.1007/978-981-32-9298-7_5" @default.
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