NASA SBIR 2002 Solicitation

SMALL BUSINESS CONCERN: (Firm Name, Mail Address, City/State/ZIP, Phone)
SMH Consulting
2664 Wild Turkey Lane
Alexandria , VA   22314 - 5814
(703 ) 567 - 2411

PRINCIPAL INVESTIGATOR/PROJECT MANAGER: (Name, E-mail, Mail Address, City/State/ZIP, Phone)
Stephanie Hulina
smhulina@yahoo.com
2664 Wild Turkey Lane
Alexandria , VA   22314 - 5814
(703 ) 567 - 2411

TECHNICAL ABSTRACT (LIMIT 200 WORDS)
Limitations with existing cloud cover detection techniques for large dataset processing and new challenges presented by the increase in the quantity and quality of data in the commercial realm, offer an opportunity for R&D into new and improved methods for the detection of clouds and cloud shadows in acquired imagery. We propose to develop innovative system for automated pixel-based cloud and cloud shadow detection. The novel, iterative, self-guided approach will rely on spectral, spatial, and contextual information from a limited number of bands (R-G-B or R-G-B-NIR) and will be applicable to large datasets of a wide range of commercial and government space- and air-borne imagery. Phase II will continue the algorithm development and refinement, and include formal algorithm validation and optimization. Further, a prototype turn-key system will be developed.

Our innovative algorithms address the need for streamlined acquisition and automated processing of very large volumes of RS data. They fill NASA?s technology needs and fit into the overall NASA mission by:
? Employing rapid analysis methodologies and algorithms,
? Improving the automated process of quality assurance /quality control for science data products, and
? Facilitating the efficient collection of data.

POTENTIAL NASA COMMERCIAL APPLICATION(S) (LIMIT 150 WORDS)
The LDCM, the next generation Landsat sensor will be operational by late 2006 / early 2007. It will contain no thermal bands. Therefore, the existing Landsat ACCA algorithms, which utilize thermal data to identify clouds, cannot be extended to LDCM. Because our automated algorithms require only visible and infrared bands and were extensively tested on Landsat imagery, they will be readily applicable to the LDCM data. The output from our system can also be used as part of the Landsat Long-term Acquisition Plan (LTAP). It can be conveyed back to the scheduling system as an indicator of past success.

POTENTIAL NON-NASA APPLICATION(S) (LIMIT 150 WORDS)
Based on a thorough understanding of the industry, in-depth literature reviews, and in-person meetings with several companies and agencies that provide space- or aerial-based data to the industry, we are confident that our cloud and cloud shadow detection system will have numerous NASA and non-NASA commercial applications. The results of our system can be used to:
? Automatically update the cloud cover percentage metadata tag (QA/QC)
? Generate a cloud and cloud shadow mask as an additional layer sold to the end-user
? Reschedule failed acquisitions
? Assess cloud cover contamination in real-time mode, i.e. on board, during the data acquisition
? Substitute cloud and cloud shadow pixels representing data loss
? Develop historic cloud cover dataset with spatial and temporal resolutions higher then those currently available
? Monitor cloud cover in near-real time mode and assess its trend
? Forecast cloud cover from historic and actual cloud data
? Formulate reliable cloud avoidance strategies through complimentary use of historical and actual cloud data