Automated Near-Real Time Cloud and Cloud Shadow Detection in ...
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Automated Near-Real Time Cloud and Cloud Shadow Detection in ...
GDA Corp. Automated Near-Real Time. Cloud and Cloud Shadow Detection in
High Resolution VNIR Imagery. JACIE. November 8-10, 2004. Stephanie Hulina
...
Automated Near-Real Time Cloud and Cloud Shadow Detection in High Resolution VNIR Imagery
JACIE November 8-10, 2004 Stephanie Hulina & Dmitry Varlyguin Geospatial Data Analysis Corp.
GDA Corp.
Presentation Outline
Project Overview Core Technology Current Development Status Next Steps / Critical Action Items Future Directions Acknowledgements
GDA Corp.
Project Overview CASA—Cloud And Shadow Assessment CHALLENGE Fully automated, per-pixel cloud and cloud shadow detection in medium and high-resolution RS data without the use of thermal data. SOLUTION CASA System—An innovative set of algorithms based on spectral, spatial and contextual information, and hierarchical self-learning logic. Near-real time, fully automated, per-pixel detection limited to VNIR data. VALUE Reduce labor & operating costs for cloud identification, and QA/QC; create new products from data previously considered loss; achieve detection rates which would allow on-board, (near)-real time cloud detection; and contributes innovative R&D on AFE GDA Corp.
CASA Algorithm Architecture Image
Image Metadata
Data PreProcessing
Ancillary Information
Automated Cloud and Shadow Assessment (CASA) Feature Library
Cloud Potential Cloud Cloud Shadow Imagery (c) Space Imaging LLC
GDA Corp.
CASA Examples 1
Imagery (c) Space Imaging LLC
2
3
4
Cloud Potential Cloud Cloud Shadow
GDA Corp.
CASA—Current Development Status One year into 2-year Phase II NASA SBIR Prototype is fully automated Straight C++ implementation except for Image I/O and User Interface Current runtime is 10-12 minutes for Landsat 7 and 4-7 minutes for Ikonos running on a 2GHz Pentium with only minimal algorithm/code optimizations to date Early prototype tested on ~ 200 Landsat and Ikonos scenes for various seasons and regions Sensor
Leaf-on
Leaf-off
agreement (%) disagreement (%) agreement (%)
disagreement (%)
Landsat 5 TM
91
9
75
25
Landsat 7 ETM+
90
10
92
8
Average (%)
91
9
87
13
GDA Corp.
Next Steps / Critical Action Items Next Steps z z z z z
Version 1: January/February 2005 (Landsat) Landsat 7 formal validation study Algorithm revisions & optimizations Benchmarking Final system delivered to NASA: January 2006
Action Items z
Extend CASA to other commercial sensors: Looking for commercial validation datasets as well as validation partners
Goals: z z
z
Commercial-grade system: February 2006 Accuracy: within 10% of the truth for 95% of all scenes processed Runtime: real- to near-real time GDA Corp.
Future Directions Extending this technology to other applications & features Examples include, but not limited to: • Enhancement of features located under shadows • Automated AFE/ATR • Rapid analysis of LULC change through innovative change detection techniques • Automated updates of road & hydrology networks and building footprints Phase III Contract Vehicle: Work that “derives from, extends, or logically concludes efforts performed under prior SBIR funding agreements” (Products, production, services, R/R&D); No competition is required –can be “sole source”; Small business limits do not apply; funding can come from any Federal agency. GDA Corp.
Acknowledgements Funding and Technical Management z NASA Small Business Innovative Research (SBIR) Program z Tom Stanley, NASA SSC Data z Spatial Data Purchase (SDP) Program at NASA SSC z Space Imaging LLC z EarthData International
