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 design1. 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.

In this paper, a selective data compression scheme is developed to combine the need for efficient data transmission and the receivers' interest in changes presented in the data by taking the advantage of the fact that receivers hold a copy of previous data. Three-step pre-processing is introduced in this paper. Firstly we separate the unchanged areas (the majority) of the image from the changed areas between successive images of the same area. Secondly, the bands which are sensitive to the changes are identified with the aid of statistical measures. Finally, a binary index image of each band is generated to indicate the two categories. Following this pre-processing, compression of the unchanged areas and changed areas is conducted separately. In this way, different compression algorithms can be applied to each case. As the majority data will be unchanged and only a subset of bands reflects the changes, high compression rate is achievable.