期刊
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
卷 53, 期 5, 页码 2587-2602出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2014.2361919
关键词
Dictionary learning (DL); image similarity; interferometric phase estimation; online learning; phase estimation; phase unwrapping; sparse regression
类别
资金
- Portuguese Science and Technology Foundation [PEst-OE/EEI/LA0008/2013, PTDC/EEI-PRO/1470/2012]
- Academy of Finland [138207]
- China Scholarship Council [2011611073]
- China Scholarship Council through the Doctor Project of the National University of Defense Technology [B110503]
- National Natural Science Foundation of China [61103081]
This paper addresses interferometric phase image estimation, i.e., the estimation of phase modulo-2 pi images from sinusoidal 2 pi-periodic and noisy observations. These degradation mechanisms make interferometric phase image estimation a quite challenging problem. We tackle this challenge by reformulating the true estimation problem as a sparse regression, often termed sparse coding, in the complex domain. Following the standard procedure in patch-based image restoration, the image is partitioned into small overlapping square patches, and the vector corresponding to each patch is modeled as a sparse linear combination of vectors, termed the atoms, taken from a set called dictionary. Aiming at optimal sparse representations, and thus at optimal noise removing capabilities, the dictionary is learned from the data that it represents via matrix factorization with sparsity constraints on the code (i.e., the regression coefficients) enforced by the l(1) norm. The effectiveness of the new sparse-coding-based approach to interferometric phase estimation, termed the SpInPHASE, is illustrated in a series of experiments with simulated and real data where it outperforms the state-of-the-art.
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