Correction of Sunglint Effects in High Spatial Resolution Hyperspectral Imagery Using SWIR or NIR Bands and Taking Account of Spectral Variation of Refractive Index of Water
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Hyperspectral Remote Sensing
Submission Deadline: September 15, 2021 (Open) Submit Now
Lei Wang, PhD
Professor, Department of Geography and Anthropology, Louisiana State University, Baton Rouge, LA 70803, USA
Research interest: Hydrology; geographic information system; hydrological modeling; mapping; satellite image analysis; satellite image processing; geospatial science; geoinformation; geographic information systems; environment remote sensing; advanced geographic information systems; advanced remote sensing
About This Topic
I am excited to announce the kickoff of the special issue: Hyperspectral Remote Sensing (HRS) on Advances in Environmental and Engineering Research. Although HRS has been widely used in many areas including water quality, agriculture, geology, limnology, and atmospheric sciences since its first introduction in 1983, it has never been such a grand occasion as right now for the applications of HRS because of that hyperspectral sensors have become affordable to individual research entities, and that two recent technological advances - drones (UAV) and artificial intelligence- provide essential support for data acquisition and information extraction. With airplanes still dominating HRS data acquisition, a blooming of drone-based HRS is evident in the current literature. Technical challenges remain as the obstacle in the applications of HRS, which include image noise, band correlation, high dimensionality, absorption feature overlapping, and geometric distortion. I invite you to contribute to this special issue by submitting your recent work as technical articles or review papers of the applications of HRS.
Interesting topics are but not limited to:
1. Satellite-based HRS
2. UAV platforms and sensors
3. Atmospheric correction
4. Data noise reduction
5. Dimension reduction and feature extraction
6. Band ratio and band selection
7. Vegetation phenology and mapping
8. Soil and mineral spectroscopic analyses
9. Water quality mapping
10. Geologic feature mapping
11. Geometric correction of HRS images
12. Deep learning and data mining of HRS data
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