科技报告详细信息
Performance of the MTI Dense-Cloud Mask Algorithm, and Its Refinement with a Genetic Learning Program.
Lewis Hirsch, K. ; Brumby, S. P. ; Harvey, N. R. ; Davis, A. B.
Technical Information Center Oak Ridge Tennessee
关键词: Multispectral photography;    Clouds;    Thermal mapping;    Algorithms;    Cloud masks;   
RP-ID  :  PB2001105676
学科分类:工程和技术(综合)
美国|英语
来源: National Technical Reports Library
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【 摘 要 】

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.

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