This document proposes a registration method for cloudy satellite images utilizing global and local thresholds. It involves determining cloud candidates through optimal thresholding and k-means clustering, followed by classifying them into thick clouds, thin clouds, and ground. The method demonstrates a segmentation accuracy of 93.29% and significantly improves registration accuracy by 24.83% over conventional methods.

1. ROBUST REGISTRATION OF CLOUDY SATELLITE IMAGES USING TWO-STEP
SEGMENTATION
ABSTRACT
In this letter, we propose an effective registrationmethod for cloudy satellite images
based on global and localthresholds. First, cloud candidates are determined by using
optimalthreshold and k-means clustering. Then, using the localthreshold, the cloud candidates are
further classified into threecategories: thick clouds, thin clouds, and ground. Finally,
accurateregistration is performed by eliminating features relating to cloudyareas. The
experiments show that the proposed method providessegmentation accuracy of 93.29%. In
addition, registration accuracyis improved by 24.83%, as compared with conventionalmethods.