Remote sensing image atmospheric correction method

Abstract

The invention discloses a remote sensing image atmospheric correction method which comprises the following specific steps of: 1) constructing a look-up table; 2) calculating the apparent reflectance; 3) acquiring angle information; 4) acquiring the optical thickness of aerosol: obtaining the optical thickness of aerosol by utilizing HJ--1 satellite CCD (Charged Coupled Device) data inversion based on a pure pixel method; 5) obtaining atmospheric correction parameters by utilizing pixel imaging geometry and optical thickness interpolation; and 6) carrying out atmospheric correction on pixels one by one, so as to obtain the reflectance ratio of the ground surface. Compared with a dark pixel method, the remote sensing image atmospheric correction method has the advantages that the obtained reflectance ratio is more approximate to the actually measured reflectance ratio of the ground surface, and the correction accuracy is greatly improved.

Description

technical field [0001] The invention relates to the field of remote sensing, in particular to a method for correcting the atmosphere of remote sensing images. Background technique [0002] In order to reduce the influence of atmospheric blur effect on satellite remote sensing images and improve the application ability of satellite remote sensing data, people have carried out extensive research on atmospheric correction of remote sensing data. The earliest research began in the 1970s. After years of development, many Atmospheric correction methods can be roughly classified into two types: the correction method based on the image itself and the correction method based on the atmospheric radiative transfer model. [0003] Atmospheric correction methods based on image features require known or assumed albedo values ​​of certain pixels in the image to establish the relationship between surface albedo and satellite observations, and assume that the entire image has the same atmosp...

AI Technical Summary

Problems solved by technology

In 2007, the MODIS algorithm (C5) corrected the relationship between the surface reflectance of visible light and near-infrared bands, and after using more diverse and more accurate aerosol models on a global scale (based on the global aerosol ground-based observation network AERONET ), the inversion accuracy has been greatly improved compared with the previous C4 version (see the figure below), but the inversion results of the upgraded algorithm in some urban areas are not satisfactory

Moreover, even though the dense vegetation algorithm is feasible in urban areas, since the CCD camera of the Environment-1 satellite does not have a mid-infrared band, the surface reflectance of dense vegetation cannot be obtained, so this algorithm cannot be used for the Environment-1 satellite

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