Advanced Remote Sensing Technologies and Their Applications
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Earth Sciences".
Deadline for manuscript submissions: 30 April 2025 | Viewed by 69
Special Issue Editors
Interests: remote sensing technology; image processing; remote sensing applications; geosciences
Special Issues, Collections and Topics in MDPI journals
Interests: intelligent representation and calculation of geological information; geological environment monitoring and evaluation; geospatial information
Special Issues, Collections and Topics in MDPI journals
Interests: remote sensing geology (geological resources, geological disasters and environment); comprehensive resource exploration and quantitative prediction; multi-source remote sensing comprehensive technology application (hyperspectral, multi-spectral, microwave, thermal infrared); resource and environmental big data and intelligent algorithms
Special Issue Information
Dear Colleagues,
With the increase and progressive development of remote sensing satellites and airborne sensors, it has become possible to acquire different types of data, enabling us to analyze the characteristics of the Earth’s surface and distinguish geological formations and units. The combined use of advanced technologies, such as deep learning, which was inspired by brain neural science, can enable the automatic learning of high-level semantic features from remote sensing images, offering a more refined level of accuracy than earlier remote sensing technologies. Based on this background, this Special Issue addresses hyper-spectral/multi-spectral image classification, unmixing, image fusion and sharpening, artificial intelligence and machine learning, lithological mapping, and other geological applications related to remote sensing.
Prof. Dr. Ke Wu
Dr. Tao Chen
Dr. Yuanjin Xu
Guest Editors
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Keywords
- lithological mapping
- deep learning
- remote sensing imagery processing
- image classification
- image fusion and sharpening
- artificial neural network
- geological remote sensing applications
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