Radiometric normalization of optical remote sensing imagery

Sensor viewing angle effects, which are caused mainly by an atmosphere and a sun-sensor-target geometry, are observed quite often in images acquired by optical remote sensing sensors, especially airborne sensors with a wide field of view. We propose an image-based empirical radiometric normalization method, which is based on a linear regression applied over linear models between the observed radiance and the target radiance for each surface class separately. The experiments for data acquired by airborne multispectral scanner DAEDALUS AADS 1268 ATM show the effectiveness and potential of the proposed method especially for the mosaicking and classification applications.