ROAD CLASSIFICATION AND CONDITION DETERMINATION USING HYPERSPECTRAL IMAGERY

15 citations as recorded by crossref.

1. RETRACTED: Identifying Asphalt Pavement Distress Using UAV LiDAR Point Cloud Data and Random Forest Classification Z. Li et al.
2. Quantifying the Value of Satellite-Based Pavement Monitoring in Partially Observable Stochastic Environments M. Bashar & C. Torres-Machi
3. Application of Optical Satellite Data in Pavement Management: Assessing Its Value in Partially Observable Stochastic Environments M. Shahri & S. Kim
4. Classification of Road Types using Convolution Neural Network and Continuous Wavelet Transform of Sound Signal C. Lee et al.
5. Integration of Field and Laboratory Spectral Data with Multi-Resolution Remote Sensed Imagery for Asphalt Surface Differentiation A. Mei et al.
6. Assessment of Asphalt Pavement Skid Resistance Using Ground-Based and UAV-Based Hyperspectral Synergy Q. Xia et al.
7. Development of spectral indices for roofing material condition status detection using field spectroscopy and WorldView-3 data S. Samsudin et al.
8. Pavement Crack Detection from Hyperspectral Images Using a Novel Asphalt Crack Index M. Abdellatif et al.
9. Hyperspectral assessment of road pavement condition using machine learning: a comparative study with EnMAP and PRISMA data H. Haghighi et al.
10. Reflectance spectroscopy of asphalt pavement under moisture and diesel perturbations: A laboratory study R. Deo et al.
11. A heuristics-based method for obtaining road surface type information from mobile lidar for use in network-level infrastructure management S. Neupane & N. Gharaibeh
12. Surface Characteristics of Ageing Asphalt Binder Coupling Thermal Oxidation and Ultraviolet Radiation B. Yang et al.
13. Review on Active and Passive Remote Sensing Techniques for Road Extraction J. Jia et al.
14. A New Method for Automated Monitoring of Road Pavement Aging Conditions Based on Recurrent Neural Network X. Chen et al.
15. Tire Road Friction Coefficient Estimation for Individual Wheel Based on Two Robust PMI Observers and a Multilayer Perceptron S. Tao et al.