## Abstract

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.

Original language | English |
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Pages (from-to) | 2587-2602 |

Number of pages | 16 |

Journal | IEEE Transactions on Geoscience and Remote Sensing |

Volume | 53 |

Issue number | 5 |

DOIs | |

Publication status | Published - May 2015 |

Publication type | A1 Journal article-refereed |

## Keywords

- Dictionary learning (DL)
- image similarity
- interferometric phase estimation
- online learning
- phase estimation
- phase unwrapping
- sparse regression
- SYNTHETIC-APERTURE RADAR
- REPRESENTATION
- ALGORITHM
- DICTIONARIES
- REGRESSION
- TRANSFORM
- RECOVERY
- FILTER

## Publication forum classification

- Publication forum level 1