Losslesss transmission of remote sensed multitemporal images by multiple single linear model

Frequently collected multitemporal multispectral images mostly present strong temporal redundancies that can be exploited for data compression in temporal domain transmission considering the fact that the user already has a previous reference image. While the single linear regression model for temporal prediction can be applied, the compression ratio is limited. In order to minimize the model residual for prediction and improve compression rate, we introduce a multiple model to cope the temporal data regression. The model parameters are optimized automatically to achieve minimum residual entropy for lossless compression. Experimental results demonstrate that the proposed method is effective, especially when the new data are not highly correlated to the previous data due to the real changes experienced between the two data collection dates.