We propose a progressive transform-based phase unwrapping (PU) technique that employs a recursive structure. Each stage, which is identical with others in the construction, performs PU by FFT method that yields a solution and a residual phase error as well. The residual phase error is then reprocessed by the following stages. This scheme effectively improves the gradient estimate of the noisy wrapped phase image, which is unrecoverable by conventional global PU methods. Additionally, by incorporating computational strength of the transform PU method in a recursive system, we can realize a progressive PU system for prospective near real-time topographic-mapping radar and near real-time medical imaging system (such as MRI thermometry and MRI flow imager). PU performance of the proposed system and the conventional PU methods are evaluated by comparing their residual error quantitatively with a fringe-density-related error metric called FZX (fringe's zero-crossing) number. Experimental results for simulated and real InSAR phase images show significant, progressive improvement over conventional ones of a single-stage system, which demonstrates the high applicability of the proposed method.

Two-dimensional phase unwrapping (PU) process usually causes a noise-induced distortion in the geographical information of a wrapped phase image obtained by, for example, interferometric synthetic aperture radar (InSAR). This paper presents a novel method to reduce the phase-unwrapping distortion by being based on two-dimensional fractional Brownian motion (fBm) theory. The method incorporates fractal geometry estimation with conventional global-transform PU. For the spatial-frequency spectrum of an observed phase image, we estimate the fractal dimension by assuming an almost constant dimension over the image. Then, according to the estimation, we compensate the distorted spectrum of the tentatively computed global PU result. We obtain a better topographical map as the inverse Fourier transform of the compensated spectrum. It is demonstrated that the proposed method increases the signal-to-noise ratio of PU results for simulated data with various noise levels. Evaluations on an actual InSAR phase image also show that the method significantly improves the quality of the conventional global-transform PU result, in particular in its fine structure.

We propose an adaptive complex-amplitude texture classifier that takes into consideration height as well as reflection statistics of interferometric synthetic aperture radar (SAR) images. The classifier utilizes the phase information to segment the images. The system consists of a two-stage preprocessor and a complex-valued SOFM. The preprocessor extracts a complex-valued feature vectors corresponding to height and reflectance statistics of blocks in the image. The following SOFM generates a set of templates (references) adaptively and classifies a block into one of the classes represented by the templates. Experiment demonstrates that the system segments an interferometric SAR image successfully into a lake, a mountain, and so on. The performance is better than that of a conventional system dealing only with the amplitude information.