Abstract
The problem, whose targets can not be effectively identified for airborne remote sensing images, is mainly due to the atmospheric scattering effect. This problem is necessary to be overcome. According to the statistical evaluations method and the different characteristics of polarization between the objects radiance and atmospheric path radiation, a new atmospheric correction method for airborne remote sensing images was proposed. Using this new method on the airborne remote sensing images which acquired on the north coast areas of China during the haze weather, we achieved a high quality corrected atmosphere-free image. The results demonstrate the power of the method on the harbor area. The results show that the algorithm, improving image contrast and image information entropy, can effectively identify the targets after atmospheric correction. The image information entropy was enhanced from 5.59 to 6.62. The research provides a new and effective atmospheric correction technical approach for the airborne remote sensing images.
Highlights
Airborne remote sensing plays an important role in searching materials of interest [1], as well as an important platform of national science missions for remote sensing monitoring
It is especially important that the aerial remote sensing can provide real-time, actionable data, automatic detection, and classification
The data acquired by aerial remote sensing platform consists of objects radiance and atmospheric path radiance information
Summary
Airborne remote sensing plays an important role in searching materials of interest [1], as well as an important platform of national science missions for remote sensing monitoring. Different methods are used to solve the direct transfer radiative problems; for example, they provide atmospheric parameters for atmospheric correction, such as 6S (Second Simulation of the Satellite Signal in the Solar Spectrum) model [5, 6]. It provides a computer code which can accurately simulate atmospheric radiative. The main advantages of statistical evaluations algorithm are that it is effective and easy to estimate the parameters These parameters are necessary for the airborne image correction. We construct the airborne polarization remote sensing atmospheric correction model which combined polarization information and statistical evaluations method. The image contrast and entropy are increased dramatically when this algorithm is applied in the airborne polarization remote sensing image acquired in the haze weather
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