A Semiphysical Approach of Haze Removal for Landsat Image

Abstract

The presence of haze could seriously contaminate the observations of optical satellite imagery. Haze not only significantly affects the visual interpretation but also reduces the accuracy of map products. In this article, a semiphysical approach is proposed to reduce the haze effects for Landsat image. The proposed approach is based on the physical model of radiative transfer theory and the presence of dark objects. As the depth map of satellite remotely sensed image is almost a constant value, the coarse transmission map of atmosphere is estimated by the haze thickness, other than the scene depth map. The derived coarse transmission is utilized to correct the color shift induced by airlight. For haze veiled textural information, the guided-filter based approach is adopted to refine the coarse transmission map to restore the textural information. Experiments are conducted upon the images acquired by Landsat at different dates and spatial locations. The visual interpretation upon the dehazed results suggests that the proposed approach could generate visually promising results and preserve spectral properties of land surfaces. Moreover, it also performs favorably against several state-of-the-art deep learning based methods and a classic algorithm. The results of quantitative assessments demonstrate that the developed approach could enhance the information of Landsat scene with minimum spectrum changes. With the visual and quantitative assessments, we can conclude that the proposed approach is a promising solution for Landsat image dehazing. It is expected to improve the data quality of hazy scene and expand the usability of Landsat data.

LFD-Net: Lightweight Feature-Interaction Dehazing Network for Real-Time Remote Sensing Tasks

“…Most of the earliest dehazing methods are based on prior knowledge. For instance, Dark Channel Prior (DCP) makes an approximation that haze-effected pixels have at least one relatively low intensity value among RGB channels [7]; a semi-physical guided-filter based approach is adopted to refine the coarse haze thickness map to restore textural information [8]; depth estimation and image segmentation are incorporated with Dark Channel Prior to generate the final transmittance [9]. These prior knowledge based methods are typically subject to empirical or statistical regularities, leading to limited application scenarios.…”