Service interruption on Monday 11 July from 12:30 to 13:00: all the sites of the CCSD (HAL, Epiciences, SciencesConf, AureHAL) will be inaccessible (network hardware connection).
Skip to Main content Skip to Navigation
Conference papers

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.
Complete list of metadata

Cited literature [19 references]  Display  Hide  Download

https://hal.inria.fr/hal-01813781
Contributor : Ihsen Hedhli Connect in order to contact the contributor
Submitted on : Saturday, June 15, 2019 - 5:39:14 AM
Last modification on : Wednesday, February 2, 2022 - 3:51:02 PM

File

JZ-Purdue-ICIP'19.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-01813781, version 2

Collections

Citation

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⟩

Share

Metrics

Record views

325

Files downloads

312