Landsat-8 OLI Multispectral Image Dehazing Based on Optimized Atmospheric Scattering Model

Optical satellite images are often affected by haze atmospheric conditions, which degrades the quality of remote sensing (RS) data and reduces the accuracy of interpretation and classification. Hence, haze removal becomes a necessary preprocessing step for most of the applications of RS image. In this article, we propose a novel haze removal method for Landsat-8 OLI multispectral image based on an optimized atmospheric scattering model. We focus on adaptively estimating the haze transmission map of each band by taking into account the effect of both wavelength and haze atmospheric conditions (haze particle size and haze particle concentration) thus improving dehazing performance. The experimental results on Landsat-8 OLI multispectral images show that the proposed dehazing model is able to remove haze successfully and significantly improve the image visibility as well as correct the spectral bias to some degree. Moreover, this method is simple and feasible, and has good practical value.