Beyond Supervised Learning in Remote Sensing: A Systematic Review of Deep Learning Approaches
Benyamin Hosseiny(University of Tehran), Jocelyn Chanussot(Institut polytechnique de Grenoble), Masoud Mahdianpari(Memorial University of Newfoundland), Ali Radman(Memorial University of Newfoundland), Mohammadali Hemati(Memorial University of Newfoundland), Fariba Mohammadimanesh(Centre For Cold Ocean Resources Engineering)
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
September 19, 2023
Cited by 50
Related Papers
Hyperspectral Image Classification—Traditional to Deep Models: A Survey for Future Prospects
|IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing|2021|393
A Systematic Review of Landsat Data for Change Detection Applications: 50 Years of Monitoring the Earth
|Remote Sensing|2021|228
Integrating SAR and Optical Data for Aboveground Biomass Estimation of Coastal Wetlands Using Machine Learning: Multi-Scale Approach
|Remote Sensing|2024|30
Urban methane emission monitoring across North America using TROPOMI data: an analytical inversion approach
|Scientific Reports|2024|22
Iranian wetland inventory map at a spatial resolution of 10 m using Sentinel-1 and Sentinel-2 data on the Google Earth Engine cloud computing platform
|Environmental Monitoring and Assessment|2023|18