Adjustment of Measurements With Multiplicative Random Errors and Trends

Measurements in remote sensing geodesy have been well known to be of speckle noise nature. Although a number of despeckling algorithms have been proposed mainly based on the local weighted statistics in the engineering literature, there are relatively few studies on the statistical adjustment methods for processing the measurements contaminated with the speckle or multiplicative errors. We develop the least squares (LS)-based adjustment methods for the remote sensing measurements with multiplicative errors and trends, evaluate the accuracy of the parameter estimates, and derive the corresponding formulas to estimate the variance of the unit weight. Simulation examples are used to illustrate the developed theory and methods.