Our study investigates interferometric SAR (InSAR) post-processing height retrieval techniques. We explore the possible improvements by adding a third satellite to the two already in orbit, and examine some potential uses of this setup. As such, we investigate three methods for height retrieval and compare their results with the original 2-satellite method. The first approach is data averaging; a simple method that extends from the results obtained using the 2-satellite method. The 3 sets of data obtained per sampling look are grouped into pairs, and the 2 statistical best pairs are selected to be averaged, producing a better estimate of the digital elevation map (DEM) height. The second approach is the unambiguous range magnification (URM) method, which seeks to ease the reliance on phase unwrapping steps often necessary in retrieving height. It does so by expanding the wrapped phase range without performing any phase unwrapping, through the use of different wrapping speeds of the 3 sets of satellite pairings. The third method is the maximum likelihood estimation technique, an asymptotically efficient method which employs the same phase expansion property as the URM to predict the closest phase estimate which best fits most (if not all) of the data sets provided.(cont.) Results show that for a handful of flyover looks, the data averaging method provides for an efficient and non-computationally intensive method for improving retrieved height results. This method can also help eliminate the need of GCPs in height retrieval, though such performance is limited by the presence of noise. The maximum likelihood method is shown to be asymptotically favorable over the data averaging method, if given a large number of flyover looks. The URM method performs worst, because it depends on the shortest baseline, which is most sensitive to noise, for unwrapping. Results are entirely simulation-based, using the engineering tool Matlab Version 6.1. Single- and multiple- trial simulations are compared for 1-dimensional interferograms only. In most cases, the root-mean-square error will be used as the metric for comparison.by Wallace D. Wong.Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005.Includes bibliographical references (p. 125-128)
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