Multi-temporal FFT regression

Sequential data transmission regarding multi-temporal image analysis is mainly dependent upon prediction or forecasting. The transmission time can be substantially reduced by properly exploiting the temporal correlation. Multi-temporal images are often affected by sensor, and illumination variations, non-uniform attenuation, atmospheric absorption and other environmental effects which render system changes in them. Most of these changes are gradual and incremental. So a recent image can be predicted from a previous sequence of images if the amount of real land-cover change is limited. Regression based prediction is the most appropriate one in this case as it can quantify the relationships between images obtained by different measurement systems in different environments. FFT regression based temporal prediction is proposed in this paper whereby the least-squares minimization is conducted on the amplitude matrices of the readings via the FFT. For a given model, the value of squared coefficient of determination (R) is always increased beyond the value obtained by conventional regression which is a common quality measure of the chosen model.