Extraction of Roads Using the Archimedes Tuning Process with the Quantum Dilated Convolutional Neural Network
Road network extraction is a significant challenge in remote sensing (RS). Automated techniques for interpreting RS imagery offer a cost-effective solution for obtaining road network data quickly, surpassing traditional visual interpretation methods. However, the diverse characteristics of road netw...
Main Authors: | Mohd Jawed Khan, Pankaj Pratap Singh, Biswajeet Pradhan, Abdullah Alamri, Chang-Wook Lee |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2023-10-01
|
Series: | Sensors |
Subjects: | |
Online Access: | https://www.mdpi.com/1424-8220/23/21/8783 |
Similar Items
-
Archimedes optimisation algorithm quantum dilated convolutional neural network for road extraction in remote sensing images
by: Arun Mozhi Selvi Sundarapandi, et al.
Published: (2024-03-01) -
D-FusionNet: road extraction from remote sensing images using dilated convolutional block
by: Ruixuan Zhang, et al.
Published: (2023-12-01) -
AGD-Linknet: A Road Semantic Segmentation Model for High Resolution Remote Sensing Images Integrating Attention Mechanism, Gated Decoding Block and Dilated Convolution
by: Yinan Jiang, et al.
Published: (2023-01-01) -
A novel dilated convolutional neural network model for road scene segmentation
by: Yachao Zhang, et al.
Published: (2022-01-01) -
Pembangunan modul archimedes bagi tajuk prinsip archimedes /
by: 524103 Nor Fadzilah Abdul Sukor, 1986- , author, et al.
Published: (2012)