Reconstruction of satellite images by multi-temporal gradient based sequential prediction

The presence of atmosphere can cause obstructions to satellite remote sensing by absorbing and scattering the electromagnetic energy. Therefore, transmittance of the atmosphere is an important factor to consider in a sensing system design
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. Also the weather conditions such as the levels of the haze, dust or mist present in the environment, introduce distortion. Relative distributions of the brightness values of images can be different depending on the seasonal effect, termed radiometric inconsistency, which is solely dependent upon the solar radiation, illumination and reflectivity effects of the object and the conditions of the atmosphere during that time. Since they change frequently, multi-temporal data have low consistency over time. The inconsistency present in the remote sensed satellite images taken for sequential analysis can cause misguiding informaiton widely used in a range of oceanographic, terrestrial and atmospheric applications, such as land-cover mapping, environmental monitoring and disaster management. Degraded multi-temporal images needs to be checked and reconstructed before it can be used. In this paper a gradient adjusted temporal prediction approach has been used to predict or approximate the recent corrupted image using previous reference image.