Haze detection, perfection and removal for high spatial resolution satellite imagery

Abstract

We present a technique to remove spatially varying haze contamination for high spatial resolution satellite imagery. This technique comprises three steps: haze detection, haze perfection and haze removal. Background Suppressed Haze Thickness Index (BSHTI) in haze detection is used to indicate relative haze thickness. ‘Fill sink’ and ‘flatten peak’ routines in haze perfection are applied to correct some spurious background effects. Virtual Cloud Point (VCP) method based on BSHTI is used in haze removal. Case study using two QuickBird images (hazy and clear) of Shenyang City in China proves the effectiveness of this technique except for those regions where haze is too thick. Comparison of the overlapped region between hazy and clear images using 76 paired polygon samples shows that squared correlation coefficient of each band between the two images becomes larger than 0.7. The advantages of this technique are that aerosol transparent bands are not needed and the technique is suitable for urban remote sensing.