Road Detection with Deep Learning in Satellite Images

Subject Areas : Majlesi Journal of Telecommunication Devices

Zohreh  Dorrani ¹ (Department of Electrical Engineering, Payame Noor University, Tehran, Iran)

Received: 2022-12-29 Accepted : 2023-02-18 Published : 2023-03-01

Keywords:

References:

• Alkhelaiwi, W. Boulila, J. Ahmad, A. Koubaa, M. Driss, “An Efficient Approach Based on Privacy-Preserving Deep Learning for Satellite Image Classification,” Remote Sens. Vol.13, pp. 2221-2232, 2021.


• E McAllister, A Payo, A Novellino, T Dolphin, and E. Medina-Lopez. “Multispectral satellite imagery and machine learning for the extraction of shoreline indicators,” Coastal Engineering, 10, pp.104102-104113, 2022.


• Zarei, and M. Esmaeilpour, “Enhancing the Quality of Satellite Images Enhancing through Combination of Feature and Pixel Level Image Fusion,” Majlesi Journal of Telecommunication Devices vol. 8, no. 4, pp.141-147, 2019.


• A. Shahraki, and M. Emadi, “Improving Image Quality based on Feature Extraction and Gaussian Model,” Majlesi Journal of Telecommunication Devices, vol. 8, no. 2, pp. 35-41, 2019.


• Ghandorh, W. Boulila, S. Masood, and A. Koubaa. “Semantic segmentation and edge detection—Approach to road detection in very high resolution satellite images,” Remote Sensing, vol. 14, no. 3, 1-22, 2022.


• Wan, Z. Xie, Y. Xu, S. Chen, and Q. Qiu, “RoadNet: A Dual-Attention Network for Road Extraction from High Resolution Satellite Imagery,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. Vol. 14, pp. 6302–6315, 2021.


• Abdollahi, B. Pradhan, N. Shukla, S. Chakraborty, A. Alamri, “Deep Learning Approaches Applied to Remote Sensing Datasets for Road Extraction: A State-Of-The-Art Review,” Remote Sens. Vol. 12, pp. 1444-1452, 2020.


• Ghasemi, and M, Bayati, “Improving the Accuracy of Detecting Cancerous Tumors Based on Deep Learning on MRI Images,” Majlesi Journal of Telecommunication Devices, vol. 11, no. 2 ,2022.


• Dorrani, H. Farsi, and S. Mohamadzadeh, “Deep Learning in Vehicle Detection Using ResUNet-a Architecture,” Jordan Journal of Electrical Engineering, Volume 8, no. 2, pp. 166-178, 2022.


• Dorrani, H. Farsi, and S. Mohamadzadeh, “Edge Detection and Identification using Deep Learning to Identify Vehicles,” Journal of Information Systems and Telecommunication, vol. 3, no. 39, pp. 201-213, 2022.


• Chen, Z. Zhang, R. Zhong, L. Zhang, H. Ma, L.  Liu, “A dense feature pyramid network-based deep learning model for road marking instance segmentation using MLS point clouds,” IEEE Trans. Geosci. Remote Sens, vol. 59, pp. 784–800, 2020.


• Heidler, L. Mou, C. Baumhoer, A. Dietz, X.X. Zhu, “HED-UNet: Combined Segmentation and Edge Detection for Monitoring the Antarctic Coastline,” IEEE Trans. Geosci. Remote Sens, vol. 60, pp. 1–14, 2021.


• Badrinarayanan, A. Kendall, and R. Cipolla, “SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation,” IEEE Trans. Pattern Anal. Mach. Intell, vol. 39, pp. 2481–2495, 2017.


• Chaiyasarn, A. Buatik, H. Mohamad, M. Zhou, S. Kongsilp, and N. Poovarodom, “Integrated pixel-level CNN-FCN crack detection via photogrammetric 3D texture mapping of concrete structures,” Automation in Construction, vol. 140, pp. 104388-104398, 2022.


• Karacı, “VGGCOV19-NET: automatic detection of COVID-19 cases from X-ray images using modified VGG19 CNN architecture and YOLO algorithm,” Neural Computing and Applications, vol. 34, no. 10, pp. 8253-8274, 2022.


• Ghasemzade, “Extracting Image Features Through Deep Learning,” Majlesi Journal of Telecommunication Devices, vol. 9, no. 3, pp. 109-114, 2020.


• Ahmed, and P. Nayak, “Detection of Lymphoblastic Leukemia Using VGG19 Model,” In 2021 Fifth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), pp. 716-723, 2021.