Multi-temporal satellite images exhibit high amount of correlation in spatial, spectral and temporal domain. This high redundancies provide a high potential and a good opportunity to explore the entropy as a funtion of natural diversity. From the information-theory point of view, the potential gain from exploiting the temporal domain correlation can be estimated by quantifying the entropy relationship between two temporally dependent images. As conditioning reduces uncertainty, knowing one of the variables reduces the average uncertainty about the others in two dependent events. So the multi-temporal images is best distributed sequentially where current images can be forecasted from previous reference image. Thus, multiple dates' remote sensed images treated as a sequential data set varies in relative distributions of the brightness values depending on the reflectivity of various features. This paper mainly reflects the fact of how various geographical features influence the temporal dependency. The initial issue treated by multi-temporal image transmission lay in the areas of data reduction that in turn depend on the quantities such as entropy and mutual information, which are functions of the probability distributions that underlie the process of communication. This paper mainly exploits the energy deviation in temporal characteristics for diverse geographical features. Mainly features for urban, forestry, desert and coastal areas have been investigated. The key measure of data compaction entropy will be exploited in this case to better understand the features dependency.

Multi-temporal Image Entropy Correlation.