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

[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.

[发布机构] Technical Information Center Oak Ridge Tennessee

[学科分类] 工程和技术（综合）

[关键词] Multispectral photography;Clouds;Thermal mapping;Algorithms;Cloud masks