Combining GIS and UAVSAR Polarimetric Radar Products for Southern CA Wildfire Study
No abstract provided
Jones, Cathleen↗
Engineering topics
Lou, Yunling
Publications and source records attributed to Lou, Yunling.
Flood Mapping Using UAVSAR and Convolutional Neural Networks
We have mapped flooded areas in data collected by the NASA/JPL Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) using two convolutional neural network (CNN) image classifier architectures: U-Net and SegNet. Our study area was a region around Houston, TX, USA affected by widespread flooding in 2017 due to Hurricane Harvey. To train and test the classifiers, we manually labelled over 10000 image segments in two flight lines. Both U-Net and SegNet yielded higher accuracy than a previous non-machine learning classifier we used as a baseline. U-Net had slightly higher accuracy than SegNet. The classifiers performed better in areas with more homogeneous land cover. To independently validate the classifier accuracy we used NOAA aerial imagery, with overall accuracy around 80%. Future work includes assessing the classifier robustness in other study areas, assessing the classifier dependence on UAVSAR incidence angle, particularly for open water and bare ground, and collecting more training data, particularly in urban areas. This study demonstrates the potential of CNN image classifiers for mapping flooded areas in airborne polarimetric SAR imagery, and for land cover classification of polarimetric SAR imagery more generally.
Denbina, Michael W↗
Improving UAVSAR results with GPS, radiometry, and QUAKES topographic imager
UNKNOWN
Muellerschoen, Ronald↗
Improving UAVSAR results with GPS, radiometry, and QUAKES topographic imager
UAVSAR is NASA’s airborne interferometricsynthetic aperture radar (InSAR) platform. The instrument has been used to detect deformation from earthquakes, volcanoes, oil pumping, landslides, water withdrawal, landfill compaction, and glaciers. It has been used to detect scars from wildfires and damage from debris flows. The instrument performs well for large changes or for local small changes. Determining subtle changes over large areas requires improved instrumentation and processing. We are working to improve the utility of UAVSAR by including GPS station position results in the processing chain, and adding a topographic imager to improve estimates of topography, 3D change, and damage. We are also exploring the benefit of microwave radiometry to mitigating error from water vapor path delay. A goal is to determine 3D tectonic deformation to millimeters per year at ~100 km plate boundary scales and to understand surface processes in areas of decorrelated radar imagery.
Muellerschoen, Ronald↗
Improving UAVSAR Results with GPS, Radiometry, and QUAKES Topographic Imager
UNKNOWN
Muellerschoen, Ronald↗
Improved Absolute Radiometric Calibration of a UHF Airborne Radar
The AirMOSS airborne SAR operates at UHF and produces fully polarimetric imagery. The AirMOSS radar data are used to produce Root Zone Soil Moisture (RZSM) depth profiles. The absolute radiometric accuracy of the imagery, ideally of better than 0.5 dB, is key to retrieving RZSM, especially in wet soils where the backscatter as a function of soil moisture function tends to flatten out. In this paper we assess the absolute radiometric uncertainty in previously delivered data, describe a method to utilize Built In Test (BIT) data to improve the radiometric calibration, and evaluate the improvement from applying the method.
radar calibration↗
Onboard Radar Processor Development for Disaster Response
Natural hazards often result in significant loss of human lives, economic assets and productivity as well as significant damage to the ecosystem. Scientists have reported more frequent and intense natural disasters in recent years, which may well be attributed to climate change. Many of the disaster response efforts were hampered by lack of up-to-date knowledge of the state of the affected areas because damaged infrastructure rendered the areas inaccessible. Radar remote sensing is playing an increasingly critical role in providing timely information to disaster response agencies due to the increasing fidelity and availability of geospatial information products.
VV (vertical transmit and vertical receive)↗
UAVSAR Program: Initial Results from New Instrument Capabilities
UAVSAR is an imaging radar instrument suite that serves as NASA's airborne facility instrument to acquire scientific data for Principal Investigators as well as a radar test-bed for new radar observation techniques and radar technology demonstration. Since commencing operational science observations in January 2009, the compact, reconfigurable, pod-based radar has been acquiring L-band fully polarimetric SAR (POLSAR) data with repeat-pass interferometric (RPI) observations underneath NASA Dryden's Gulfstream-III jet to provide measurements for science investigations in solid earth and cryospheric studies, vegetation mapping and land use classification, archaeological research, soil moisture mapping, geology and cold land processes. In the past year, we have made significant upgrades to add new instrument capabilities and new platform options to accommodate the increasing demand for UAVSAR to support scientific campaigns to measure subsurface soil moisture, acquire data in the polar regions, and for algorithm development, verification, and cross-calibration with other airborne/spaceborne instruments.
P-band polarimetry↗
Overview of the NASA/JPL UAVSAR and Porting to the Global Hawk
UAVSAR has demonstrated the combined Precisions Autopilot coupled with the L-band electronically steered allow for robust repeat pass radar interferometric collections. Science results in the areas of soil moisture, volcanology, solid earth science, glaciology, and ecosystem system science have been achieved using the UAVSAR platform.
Uninhabited Aerial Vehicle Synthetic Aperture Rada↗