SBIR-STTR Award

Limitations with existing cloud cover detection (CCD) 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 software for automated pixel-based cloud and cloud shadow detection. The novel, iterative, self-guided approach will rely on spectral and spatial 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. Our techniques will be refined by algorithmic testing on a variety of image types and by consulting with industry experts on their assessment of the applicability of the algorithms to their data and to the needs of their end users. Further, in order to flesh out the market and requirements for successful commercialization, we propose to research novel methodologies and inputs to: (1) recovery and substitution of cloud and cloud shadow contaminated pixels, (2) on board real-time CCD processing, and (3) cloud cover monitoring, forecasting, and avoidance strategies. POTENTIAL COMMERCIAL APPLICATIONS Results of the cloud and cloud shadow detection technique can be used to: --Automatically (100%) 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 --Recover data in transparent cloud shadow areas --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

___(NOTE: Note: no official Abstract exists of this Phase II projects. Abstract is modified by idi from relevant Phase I data. The specific Phase II work statement and objectives may differ)___ Limitations with existing cloud cover detection (CCD) 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 software for automated pixel-based cloud and cloud shadow detection. The novel, iterative, self-guided approach will rely on spectral and spatial 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. Our techniques will be refined by algorithmic testing on a variety of image types and by consulting with industry experts on their assessment of the applicability of the algorithms to their data and to the needs of their end users. Further, in order to flesh out the market and requirements for successful commercialization, we propose to research novel methodologies and inputs to: (1) recovery and substitution of cloud and cloud shadow contaminated pixels, (2) on board real-time CCD processing, and (3) cloud cover monitoring, forecasting, and avoidance strategies. POTENTIAL COMMERCIAL APPLICATIONS Results of the cloud and cloud shadow detection technique can be used to: --Automatically (100%) 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 --Recover data in transparent cloud shadow areas --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