We propose an empirical radiometric correction method for the effects, such as atmospheric effects and anisotropic reflection of the surface, in optical remote sensing data. These distortions are sensor viewing (scanning) angle dependent, thus they can be significant for data received from airborne sensors due to their wide field of view. The procedure is based solely on the digital image data and consists of several steps. First, the initial image region near nadir (minimal distortions) is clustered by an extended k-means algorithm, which automatically detects the clusters (surface types) in an image. Then, for each cluster an average line profile is calculated. These profiles (initially defined in a middle part of an image line) are extrapolated to the whole line of an image by a polynomial approximation. Finally, from these polynomial functions the linear regression over all clusters is build using the radiative transfer equation, which allows the radiometric correction for each viewing angle in an image relative to the reference angle, usually nadir. The procedure is iterative, that is the correction is first performed for a narrow part around the initial region. Then the procedure is initialized with this newly corrected image region and repeated until the whole image is corrected. The experiments for data acquired by airborne multispectral scanner DAEDALUS AADS 1268 ATM show the effectiveness of the proposed method especially for the mosaicking and classification applications.