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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

July 2010

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Proposed Onboard Processing Scenario for DESDynI Mission’s Radar

Instrument

July 2010

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

July 2010

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

July 2010

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Onboard Product Examples:Forest Fire Extent

July 2010

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

12July 2010

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