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Performance of the MTI Dense-Cloud Mask Algorithm, and Its Refinement with a Genetic Learning Program.

PB2001105676

Publication Date	2000
Personal Author	Lewis Hirsch, K.; Brumby, S. P.; Harvey, N. R.; Davis, A. B.
Page Count	12
Abstract	In support of its dual mission in environmental studies and nuclear nonproliferation, the Multispectral Thermal Irnager (MTI) has enhanced spatial and radiometric resolutions and state-of-the-art calibration capabilities. These instrumental developments put a new burden on retrieval algorithm developers to pass this accuracy onto the inferred geophysical parameters. In particular, current atmospheric correction schemes assume the intervening atmosphere is adequately modeled as a plane parallel horizontally homogeneous medium. A single dense-enough cloud in view of the ground target can easily offset reality from the calculations, hence the need for a reliable cloudmasking algorithm. The authors have created a cloud mask using simple, user defined thresholds. This mask compares quite favorably to the MODIS cloud mask. While it does require user intervention, it does not require large amounts of processing time or disk space. They have also compared the results to an evolved solution for masking clouds. Again, the simple method compares favorably to the genetic algorithm derived mask.
Keywords	Multispectral photography Clouds Thermal mapping Algorithms Cloud masks Multispectral thermal imaging
Source Agency	Technical Information Center Oak Ridge Tennessee
NTIS Subject Category	46C - Optics & Lasers 48C - Natural Resource Surveys 55C - Meteorological Data Collection, Analysis, & Weather Forecasting
Corporate Authors	Los Alamos National Lab., NM.; California Univ., Berkeley.; Department of Energy, Washington, DC.
Supplemental Notes	Submitted to SPIE International Symposium on Optical Science and Technology, San Diego, CA, Jul 30-Aug 2000. Prepared in cooperation with California Univ., Berkeley. Sponsored by Department of Energy, Washington, DC.
Document Type	Technical Report
NTIS Issue Number	200117
Contract Number	DOE-W-7405-ENG-36

Performance of the MTI Dense-Cloud Mask Algorithm, and Its Refinement with a Genetic Learning Program.

Performance of the MTI Dense-Cloud Mask Algorithm, and Its Refinement with a Genetic Learning Program.
PB2001105676

Publication Date	2000
Personal Author	Lewis Hirsch, K.; Brumby, S. P.; Harvey, N. R.; Davis, A. B.
Page Count	12
Abstract	In support of its dual mission in environmental studies and nuclear nonproliferation, the Multispectral Thermal Irnager (MTI) has enhanced spatial and radiometric resolutions and state-of-the-art calibration capabilities. These instrumental developments put a new burden on retrieval algorithm developers to pass this accuracy onto the inferred geophysical parameters. In particular, current atmospheric correction schemes assume the intervening atmosphere is adequately modeled as a plane parallel horizontally homogeneous medium. A single dense-enough cloud in view of the ground target can easily offset reality from the calculations, hence the need for a reliable cloudmasking algorithm. The authors have created a cloud mask using simple, user defined thresholds. This mask compares quite favorably to the MODIS cloud mask. While it does require user intervention, it does not require large amounts of processing time or disk space. They have also compared the results to an evolved solution for masking clouds. Again, the simple method compares favorably to the genetic algorithm derived mask.
Keywords	Multispectral photography Clouds Thermal mapping Algorithms Cloud masks Multispectral thermal imaging
Source Agency	Technical Information Center Oak Ridge Tennessee
NTIS Subject Category	46C - Optics & Lasers 48C - Natural Resource Surveys 55C - Meteorological Data Collection, Analysis, & Weather Forecasting
Corporate Authors	Los Alamos National Lab., NM.; California Univ., Berkeley.; Department of Energy, Washington, DC.
Supplemental Notes	Submitted to SPIE International Symposium on Optical Science and Technology, San Diego, CA, Jul 30-Aug 2000. Prepared in cooperation with California Univ., Berkeley. Sponsored by Department of Energy, Washington, DC.
Document Type	Technical Report
NTIS Issue Number	200117
Contract Number	DOE-W-7405-ENG-36