An improved method for cloud removal in ASTER data change detection

ASTER data with 15m resolution in visible and near infrared bands are proper data for urban change detection. Often proper data (without cloud) cannot be acquired conveniently. So cloud removal is a very important step before making comparisons. Traditional methods have some shortcomings. Thus, based on statistical characters of image information, an improved homomorphism filtering method is put forward. Instead of the filtering in frequency field, it isolates the low frequency component of the image representing cloud information with calculating neighborhood average in spatial field. An experiment indicates that the method can obtain good results and performs faster than traditional methods.