Nonlinear Elastic Model for Flexible Prediction of Remotely Sensed Multitemporal Images

While an increasing number of satellite images are collected over a regular period in order to provide regular spatiotemporal information on land-use and land-cover changes, there are very few compression schemes in remotely sensed imagery that use historical data as a reference. Just as individual images can be compressed for separate transmission by taking into account their inherent spatial and spectral redundancies, the temporal redundancy between images of the same scene can also be exploited for sequential transmission. In this letter, we propose a nonlinear elastic method based on the general relationship to predict adaptively the current image from a previous reference image without any loss of information. The main feature of the developed method is to find the best prediction for each pixel brightness value individually using its own conditional probabilities to the previous image, instead of applying a single linear or nonlinear model. A codebook is generated to record the nonlinear point-to-point relationship. This temporal lossless compression is incorporated with spatial- and spectral-domain predictions, and the performances are compared with those of the JPEG2000 standard. The experimental results show an improved performance by more than 5%.