Closest Spectral Fit for Removing Clouds and Cloud Shadows

Completely cloud-free remotely sensed images are preferred, but they are not always available. Although the average cloud coverage for the entire planet is about 40 percent, the removal of clouds and cloud shadows is rarely studied. To address problem, a closest spectral fit method is developed to replace cloud and cloud-shadow pixels with their most similar non-clouded pixel values. The objective of this paper is to illustrate the methodology of the closest spectral fit and test its performance for removing clouds and cloud shadows in images. The closest spectral fit procedures are summarized into six steps, in which two main conceptions, location-based one-to-one correspondence and spectral-based closest fit, are defined. The location-based one-to-one correspondence is applied to identify pixels with the some locations in both base image and auxiliary images. The spectral-based closest fit is applied to determine the most similar pixels in an image. Finally, this closest spectral fit approach is applied to remove cloud and cloud-shadow pixels and diagnostically checked using Landsat TM images. Additional examples using Quick-Bird and MODIS images also indicate the efficiency of the closest spectral fit for removing cloud pixels.