FR1.L09.2 - ONBOARD RADAR PROCESSING CONCEPTS FOR THE DESDYNI MISSION
Transcript of FR1.L09.2 - ONBOARD RADAR PROCESSING CONCEPTS FOR THE DESDYNI MISSION
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Onboard Radar Processing Concepts for the DESDynI Mission
Yunling Lou, Steve Chien, Duane Clark, Joshua Doubleday, Ron Muellerschoen, and Charles Wang
Jet Propulsion LaboratoryCalifornia Institute of Technology
Pasadena, California
IGARSS 201026-30 July, 2010Honolulu, Hawaii
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Agenda
• DESDynI mission radar measurements overview
• Benefits of onboard processing technology
• Approach to onboard product development
• Examples of onboard products
• Future work
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DESDynI Mission Radar Measurements Overview
July 2010
BiomassCanopy Height
BiomassVegetation Structure
Vegetation Disturbance
Sea Ice Thickness
Simard et al (2006,2008)Simard et al (2006,2008)
Effects of changing climate on habitats and
CO2
Polarimetric SAR
Water Resource ManagementGeo-Hazards
Response of ice sheets to climate change & sea
level rise
Sea Ice andIce Sheet Dynamics
Changes in Earth’s Surface
Pass 2Pass 1
Repeat Pass InSAR8 Tbit onboard storage1 Gbps TDRSS link
580 to 1160 Mbps
2000 Mbps
Raw Data Rate
Onboard processing (OBP) will enable more frequent ecosystem and cryospheric science observations than currently feasible
Most disaster response applications would not be possible without onboard processing
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Potential Benefits of Onboard Processing Technology for DESDynI Mission
Solid Earth Ecosystem Structure Cryospheric Science
Potential Onboard Products
Compressed interferogram* Forest biomassSoil moistureVegetation classificationLand use classification
Sea ice classificationFreeze/thaw maps
Potential Downlink Data Volume Reduction Factor
10, but only for limitedscenes that can be stored on board
1000 1000 for sea ice (non-interferometric)
Potential Disaster Response Products
EarthquakeFloodingLava flow
Forest fuel loadHurricane damage in forestFlooding
Ice meltingShip channel freeze/thawing
July 2010
Interferograms and Single-look Complex (SLC) data can be compressed with ICER, a progressive, wavelet-based image data compressor designed for deep space applications such as the Mars Rover missions. ICER uses only integer math and can be implemented in FPGAs (courtesy: A. Kiely of Jet Propulsion Laboratory)
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Other Potential Advantages of Onboard Processing for Earth Science Radar Missions
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Proposed Onboard Processing Scenario for DESDynI Mission’s Radar
Instrument
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Onboard Processor
Analyze and detect featuresReprioritize downlink if needed
Modify data acquisitions if needed
DESDynIOnboard Autonomy Software
Form SAR image
Generate onboard product?
Compress SAR image inpreparation for calibration and post-processing on the ground
Generate low resolution product(e.g. sea ice classification
at 100 m resolution)
Downlink data volume reduced by a factor of 1000
Downlink data volume reduced by a factor of 10
Ground Receiving Station
No
Yes
Downlink data volume reduction factor is directly proportional to the level of onboard processing involved:
With SAR image formation followed by image compression technique, a factor of 10 in data volume reduction is expected
With SAR image formation followed by science data processing for specific feature detection applications, downlink data volume can be reduced by a factor of 1000
Ability to generate targeted products onboard will enable rapid response to natural hazards via Direct Broadcast (15 Mbps downlink rate)
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Example Mission Scenario with Autonomous Sciencecraft Experiment
Image taken by Spacecraft
Event Detection
Event Detection
No event Detected:
Delete Image
No event Detected:
Delete Image
Event Detected
Event Detected
Onboard Science Analysis
Track a wide range of science events – floods, volcanoes,
cryosphere, clouds,…
Key Insight: No need to replicate ground science
analysis – just detect activity
ASE uses state of the art Machine
Learning to detect events in the presence of
noise
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High Level Architecture of the Onboard Processor
Repetitive, computational-intensive operations are done in the FPGA Radar parameters that are calculated every range or azimuth line are done in the microprocessor Dual-channel processor can handle interferometric or dual-polarized processing
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PMC cards for high speed data transfer
FiberPre-processor (Micro-processor)
FPGAProcessor 1
FPGAProcessor 2
Post-processor (Micro-processor)
Ethernet
Raw radar data
Control interface
Control processor SAR image formation &Image compression
Custom boards
cPCI ChassisRocketIO interfaes
Product generation
≤ 2.5 Gbps
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Approach to Onboard Product Development
Determine the types of data processing suitable for implementation onboard the spacecraft to achieve the highest data compression gain without sacrificing science objectives SAR image formation
Polarimetric calibration
Multilooking
Cross-correlation
Simple classification techniques
Adapt complex science algorithms for onboard processing to generate 100 m resolution products such as forest biomass, flood scene map, sea ice classification, and correlation map for change detection
Identify product applications currently not included in the mission objectives
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Onboard Product Examples:Forest Fire Extent
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LHH, LHV, LVV
Tracking forest fire extent with UAVSAR L-band polarimetric data products
Fire scars
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Onboard Product Examples:Forest Fuel Load
July 2010
LHH, LHV, LVV
UAVSAR data over Kings Canyon
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Low Fuel Load Forests
Distribution of 1-hr Branch Fuel Load
Simplified tree crown fuel load model using LHH and LHV data is based on algorithm developed by Saatchi, et al. We collected field data in Kings Canyon to verify the fuel load determination
Onboard Product Examples:UAVSAR Fuel Products
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Medium Fuel Load Forests
Distribution of 10-hr Branch Fuel Load
Onboard Product Examples:UAVSAR Fuel Products
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High Fuel Load Forests
Distribution of 100-hr Branch Fuel Load
Onboard Product Examples:UAVSAR Fuel Products
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Onboard Product Examples:Earthquake Damage Assessment
July 2010
By comparing two post-earthquake amplitude images, we are able to identify old features (green) that have been removed (perhaps damaged buildings) and new features (red) that have been built (perhaps tent cities) over a two-week period.
Amplitude Change Detection with UAVSAR’s 16-day Repeat Pass Data over Port Au Prince Airport, Haiti
Jan 27, 2010
Feb 13, 2010
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Onboard Product Examples:Glacier Melting
July 2010
L-band polarmetric image of the Kangerlugssuaq ice fjord in Eastern Greenland. The grounding line of the glacier is easily identifiable in the image
LHH, LHV, LVVGrounding line
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Input Data: Training, Cross Validation & Evaluation
SVM Output Visual Imagery
Low statistical correlation with input data (noisy), high visual feature correlation
April 22, 2006 Courtesy Google EarthLabels for training and cross validation statistics. National Land Cover Data 2001, condensed to 4 coarse classes.
Polarimetric data, incidence angle, etc: training and final classification input.
Support Vector Machine
Onboard Product Examples:Vegetation Classification with Support Vector
Machine
water
dense veg.light veg.
bare/urban
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Future Work
• Prototype additional rapid response application products– Flood extent– Lava flow– Hurricane damage– Ice melting (ship channel and ground thawing)
• Utilize machine learning to classify some of the products for event detection and onboard replanning
• Prototype products for long term monitoring applications– Vegetation type classification– Soil moisture monitoring
July 2010