A Grouped Threshold Approach for Scene Identification in AVHRR Imagery

Abstract
The authors propose a grouped threshold method for scene identification in Advanced Very High Resolution Radiometer imagery that may contain clouds, fire, smoke, or snow. The philosophy of the approach is to build modules that contain groups of spectral threshold tests that are applied concurrently, not sequentially, to each pixel in an image. The purpose of each group of tests is to identify uniquely a specific class in the image, such as smoke. A strength of this approach is that insight into the limits used in the threshold tests may be gained through the use of radiative transfer theory. Methodology and examples are provided for two different scenes, one containing clouds, forest fires, and smoke; and the other containing clouds over snow in the central United States. For both scenes, a limited amount of supporting information is provided by surface observers. Abstract The authors propose a grouped threshold method for scene identification in Advanced Very High Resolution Radiometer imagery that may contain clouds, fire, smoke, or snow. The philosophy of the approach is to build modules that contain groups of spectral threshold tests that are applied concurrently, not sequentially, to each pixel in an image. The purpose of each group of tests is to identify uniquely a specific class in the image, such as smoke. A strength of this approach is that insight into the limits used in the threshold tests may be gained through the use of radiative transfer theory. Methodology and examples are provided for two different scenes, one containing clouds, forest fires, and smoke; and the other containing clouds over snow in the central United States. For both scenes, a limited amount of supporting information is provided by surface observers.

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