A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Reduced Image Aliasing With Microwave Radiometers and Weather Radar Through Windowed Spatial Averaging
Microwave remote sensing instruments detect and image physical phenomena such as brightness temperature and volume reflectivity. The spatial resolution of these measurements is limited by the physical properties of the instrument such as the antenna size, the spatial scan pattern, and temporal sampling. Analysis shows that common sampling schemes undersample the spatial information present at the antenna. Here, we address methods to better capture the spatial information available by applying the Nyquist-Shannon sampling theory to the spatial averaging and sampling of remote sensing data. The use of overlapping windows for spatial averaging rather than treating pixels independently improves the image fidelity while maintaining the system sensitivity. Additionally, the sensitivity to spatially small targets can be maximized by matching the window shape to the antenna pattern. The spatial imaging of scanning radiometers, radars, and phased-array systems is addressed. These principles are demonstrated with the theory and data from the National Aeronautics and Space Administration Goddard Space Flight Center's High-Altitude Imaging Wind and Rain Airborne Profiler (HIWRAP) radar.
Reduced Image Aliasing With Microwave Radiometers and Weather Radar Through Windowed Spatial Averaging
Microwave remote sensing instruments detect and image physical phenomena such as brightness temperature and volume reflectivity. The spatial resolution of these measurements is limited by the physical properties of the instrument such as the antenna size, the spatial scan pattern, and temporal sampling. Analysis shows that common sampling schemes undersample the spatial information present at the antenna. Here, we address methods to better capture the spatial information available by applying the Nyquist-Shannon sampling theory to the spatial averaging and sampling of remote sensing data. The use of overlapping windows for spatial averaging rather than treating pixels independently improves the image fidelity while maintaining the system sensitivity. Additionally, the sensitivity to spatially small targets can be maximized by matching the window shape to the antenna pattern. The spatial imaging of scanning radiometers, radars, and phased-array systems is addressed. These principles are demonstrated with the theory and data from the National Aeronautics and Space Administration Goddard Space Flight Center's High-Altitude Imaging Wind and Rain Airborne Profiler (HIWRAP) radar.
Reduced Image Aliasing With Microwave Radiometers and Weather Radar Through Windowed Spatial Averaging
Matthew L McLinden (author) / Edward J Wollack / Gerald M Heymsfield / Lihua Li
2015
Article (Journal)
English
Local classification TIB:
770/3710/5670
BKL:
38.03
Methoden und Techniken der Geowissenschaften
/
74.41
Luftaufnahmen, Photogrammetrie
Waveform Aliasing in Satellite Radar Altimetry
| Online Contents | 2015
Sensing of a Rain-Disturbed Sea Surface by Almaz-1 Radar and Microwave Radiometers
| Online Contents | 1997