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Feature extraction and tracking of CNN segmentations for improved road detection from satellite imagery

Abstract : Road detection in high-resolution satellite images is an important and popular research topic in the field of image processing. In this paper, we propose a novel road extraction and tracking method based on road segmentation results from a convolutional network, providing improved road detection. The proposed method incorporates our previously proposed connected-tube marked point process (MPP) model and a post-tracking algorithm. We present experimental results on the Massachusetts roads dataset to show the performance of our method on road detection in remotely-sensed images.
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https://hal.inria.fr/hal-01813781
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Submitted on : Saturday, June 15, 2019 - 5:39:14 AM
Last modification on : Tuesday, October 15, 2019 - 1:21:43 AM

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  • HAL Id : hal-01813781, version 2

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Tianyu Li, Mary Comer, Josiane Zerubia. Feature extraction and tracking of CNN segmentations for improved road detection from satellite imagery. ICIP 2019 - IEEE International Conference on Image Processing, Sep 2019, Taipei, Taiwan. ⟨hal-01813781v2⟩

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