Machine Learning at the Edge and Optical Image Analysis and Classification in Remote Sensing
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".
Deadline for manuscript submissions: 15 December 2024 | Viewed by 138
Special Issue Editors
Interests: deep learning; vector data rendering, and processing; GIS applications; artificial intelligent applications in GIS and RS
Special Issues, Collections and Topics in MDPI journals
Interests: image processing; 3-D rebuilding; spatial analysis; GIS; geo-computing; artificial intelligence; spatial cognition
Interests: GeoAI; urban data science and big data analytics; geospatial artificial intelligence; spatial analysis; spatial statistics; geoinformation; geospatial science; intelligent understanding of urban big data; urban functional area analysis
Interests: remote-sensing image; CycleGAN (cycle generative adversarial networks); deep learning; information recovery; weakly supervised; road extraction; remote sensing image; generative adversarial networks
Special Issue Information
Dear Colleagues,
Benefiting from the continuous progress of remote sensing acquisition technology, aerial remote sensing technology has realized the ability to make all-day observations without interference from weather and other objective reasons, making it possible to collect surface information for Earth observation in a rapid and high-quality manner. Accompanied by the global attention to Earth observation research and the rapid implementation of various Earth observation programs, all kinds of interpretation applications based on remote sensing images have been developed by leaps and bounds. Deep learning and computer vision algorithms provide the basis for intelligent processing of visible remote sensing images.
Meanwhile, as one of the important data for other research such as urban 3D modeling and urban functional area classification, the location information of various land features has always been a research focus in optical remote sensing images. With the continuous improvement of urban construction accuracy in society, research on intelligent interpretation based on optical remote sensing images focuses more on the refinement and generalization of various types of research.
Given the above reasons, the interpretation of optical remote sensing images using computer vision and deep learning algorithms is currently a research focus in the field of remote sensing, and refined interpretation results have become an important data foundation for urban construction, mainly including the following research:
- Remote sensing image object detection
- Remote sensing image change detection
- Urban functional area analysis using remote sensing image
- Building pattern recognition
- Remote sensing image cloud and fog removal
- Deep learning techniques for enhanced land use and land cover classification
- Time-series land use and land cover mapping
- Building height extraction
- Fusion of remote sensing image with multi-source data
- Quantification of CO2 emissions from remote sensing image
- Land surface temperature estimation
Dr. Yongyang Xu
Prof. Dr. Zhong Xie
Dr. Sheng Hu
Dr. Anna Hu
Guest Editors
Manuscript Submission Information
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Keywords
- deep learning
- machine learning
- remote sensing applications
- classification segmentation
- remote sensing interpretation
- pattern recognition
- height extraction
- object detection
- land use and land cover
- vision transformer model
- multi-source remote sensing data
