Object segmentation of remotely sensed aerial (or very-high resolution, VHS) and satellite (or high- resolution, HR) pictures has been used in a variety of applications, most notably in road extraction, where segmented objects are used as a required layer in geographic databases. Several attempts have been made to extract roads from remote sensing pictures using the convolutional neural network (CNN); nevertheless, the accuracy is still restricted. In this study, we offer an improved CNN system incorporating fuzzy that uses fuzzy logic in the CNN to extract roads from remote sensing pictures. The fuzzy logic is used in our network to enhance the CNN by removing the ambiguities present in the input images, resulting in a greater number of and still more accurate extracted roads. The tests used data from the Bavaria, Aerial KITTI, Vaihingen, and Potsdam data sets. On any type of remote sensing data, our suggested approach proves to be a better object segmentation technique, in most situations.
REFERENCES
1.
M.
Darweesh
, S.
Al Mansoori
, and H.
Alahmad
, “Simple Roads Extraction Algorithm Based on Edge Detection Using Satellite Images
,” 2019 IEEE 4th Int. Conf. Image, Vis. Comput. ICIVC 2019
, pp. 578
–582
, 2019
, doi: .2.
N. S.
Kumar
, B.
Sukanya
, B.
Mohan
, and G.
Prathibha
, “Extraction of Roads from Satellite Images Based on Edge Detection
,” Int. J. Eng. Dev. Res.
, vol. 5
, no. 2
, pp. 187
–190
, 2017
.3.
J.
Hormese
, “Architectures for Road Classification from Satellite
,” 2018 Int. Conf. Inven. Res. Comput. Appl.
, no. Icirca, pp. 354
–359
, 2018
.4.
H. R. R.
Bakhtiari
, A.
Abdollahi
, and H.
Rezaeian
, “Semi automatic road extraction from digital images
,” Egypt. J. Remote Sens. Sp. Sci.
, vol. 20
, no. 1
, pp. 117
–123
, 2017
, doi: .5.
J.
Hormese
and C.
Saravanan
, “Automated Road Extraction From High Resolution Satellite Images
,” Procedia Technol.
, vol. 24
, pp. 1460
–1467
, 2016
, doi: .6.
A.
Constantin
, J. J.
Ding
, and Y. C.
Lee
, “Accurate Road Detection from Satellite Images Using Modified U-net
,” 2018 IEEE Asia Pacific Conf. Circuits Syst. APCCAS 2018
, pp. 423
–426
, 2019
, doi: .7.
A.
Buslaev
, S.
Seferbekov
, V.
Iglovikov
, and A.
Shvets
, “Fully convolutional network for automatic road extraction from satellite imagery
,” IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. Work.
, vol. 2018
-June, pp. 197
–200
, 2018
, doi: .8.
L.
Caltagirone
, S.
Scheidegger
, L.
Svensson
, and M.
Wahde
, “Fast LIDAR-based road detection using fully convolutional neural networks
,” IEEE Intell. Veh. Symp. Proc.
, no. Iv
, pp. 1019
–1024
, 2017
, doi: .9.
Y.
Wei
, K.
Zhang
, and S.
Ji
, “Road Network Extraction from Satellite Images Using CNN Based Segmentation and Tracing
,” Int. Geosci. Remote Sens. Symp.
, pp. 3923
–3926
, 2019
, doi: .10.
S.
Oehmcke
, C.
Thrysoe
, A.
Borgstad
, M. A. V.
Salles
, M.
Brandt
, and F.
Gieseke
, “Detecting Hardly Visible Roads in Low-Resolution Satellite Time Series Data
,” Proc. - 2019 IEEE Int. Conf. Big Data, Big Data 2019
, pp. 2403
–2412
, 2019
, doi: .11.
T.
Li
, M.
Comer
, and J.
Zerubia
, “Feature Extraction and Tracking of CNN Segmentations for Improved Road Detection from Satellite Imagery
,” Proc. - Int. Conf. Image Process. ICIP
, vol. 2019
-Septe, pp. 2641
–2645
, 2019
, doi: .12.
Y.
Nachmany
and H.
Alemohammad
, “Detecting Roads from Satellite Imagery in the Developing World
,” 2019 IEEE Conf. Comput. Vis. Pattern Recognit.
, no. June, pp. 83
–89
, 2019
.13.
Z.
Zhong
, J.
Li
, W.
Cui
, and H.
Jiang
, “Fully convolutional networks for building and road extraction: Preliminary results
,” Int. Geosci. Remote Sens. Symp.
, vol. 2016
-Novem, pp. 1591
–1594
, 2016
, doi: .14.
Q.
Shi
, X.
Liu
, and X.
Li
, “Road Detection from Remote Sensing Images by Generative Adversarial Networks
,” IEEE Access
, vol. 6
, no. c, pp. 25486
–25494
, 2017
, doi: .15.
Z.
Hong
, D.
Ming
, K.
Zhou
, Y.
Guo
, and T.
Lu
, “Road extraction from a high spatial resolution remote sensing image based on richer convolutional features
,” IEEE Access
, vol. 6
, pp. 46988
–47000
, 2018
, doi: .16.
R.
Alshehhi
and P. R.
Marpu
, “Hierarchical graph-based segmentation for extracting road networks from high-resolution satellite images
,” ISPRS J. Photogramm. Remote Sens.
, vol. 126
, pp. 245
–260
, 2017
, doi: .17.
I.
Grinias
, C.
Panagiotakis
, and G.
Tziritas
, “MRF-based segmentation and unsupervised classification for building and road detection in peri-urban areas of high-resolution satellite images
,” ISPRS J. Photogramm. Remote Sens.
, vol. 122
, pp. 145
–166
, 2016
, doi: .18.
Z.
Zhang
, Q.
Liu
, Y.
Wang
, and S.
Member
, “Deep Residual Network ***Jii***IMa - D-¥1z***.pdf
,” pp. 1
–5
, 2018
.19.
Z.
Miao
, W.
Shi
, P.
Gamba
, and Z.
Li
, “An Object-Based Method for Road Network Extraction in VHR Satellite Images
,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens.
, vol. 8
, no. 10
, pp. 4853
–4862
, 2015
, doi: .20.
R.
Kestur
, S.
Farooq
, R.
Abdal
, E.
Mehraj
, O.
Narasipura
, and M.
Mudigere
, “UFCN: a fully convolutional neural network for road extraction in RGB imagery acquired by remote sensing from an unmanned aerial vehicle
,” J. Appl. Remote Sens.
, vol. 12
, no. 01
, p. 1
, 2018
, doi: .21.
C.
Henry
, S. M.
Azimi
, and N.
Merkle
, “Road segmentation in SAR satellite images with deep fully convolutional neural networks
,” IEEE Geosci. Remote Sens. Lett.
, vol. 15
, no. 12
, pp. 1867
–1871
, 2018
, doi: .22.
M.
Saati
and J.
Amini
, “Road network extraction from high-resolution sar imagery based on the network snake model
,” Photogramm. Eng. Remote Sensing
, vol. 83
, no. 3
, pp. 207
–215
, 2017
, doi: .23.
M.
Karaduman
, A.
Çınar
, and H.
Eren
, “UAV Traffic Patrolling via Road Detection and Tracking in Anonymous Aerial Video Frames
,” J. Intell. Robot. Syst. Theory Appl.
, vol. 95
, no. 2
, pp. 675
–690
, 2019
, doi: .
This content is only available via PDF.
© 2022 Author(s).
2022
Author(s)
You do not currently have access to this content.
