A new comparative study of radiometric correction on satellite images using Kalman filter and Levenberg Marquardt algorithm

With the development of satellite and remote sensing techniques, more and more multitemporal image data from airborne/satellite sensors have been collected and used in huge amounts to monitor the changes in land use and land cover. Radiometric consistency among collected multi-temporal imagery is difficult to maintain, because of variations in sensor characteristics, atmospheric conditions, solar angle, and sensor view angle. Radiometric corrections are used to remove the effects that alter the spectral characteristics of land features, except for actual changes in ground target, becoming mandatory in multi-sensor, multi-date studies. In this paper, a comparative analysis of radiometric correction of satellite
images is made between Kalman filter and Levenberg algorithm. In first phase, the satellite images such as Landsat, Liss-3 have been corrected using Kalman filter technique. In the second phase, by using Levenberg algorithm radiometric correction has been performed. After that comparative study is made between the results of both techniques using different performance measures such as completeness, correctness and quality.