Non-Convex Optimization For Sparse Interferometric Phase Estimation

Abstract

We present a new sparsity based technique for interferometric phase estimation. We consider complex extensions of non-convex regularizers such as the minimax concave penalty (MCP) and smoothly clipped absolute deviation penalty (SCAD) for sparse recovery. We solve the problem of interferometric phase estimation based on complex-domain dictionary learning. We develop an algorithm, namely, improved sparse interferometric phase estimation (iSpInPhase) based on alternating direction method of multipliers (ADMM) and Wirtinger calculus for solving the optimization problem. Wiritinger calculus is employed because the cost functions are nonholomorphic. We evaluate the performance of iSpInPhase on synthetic data, namely, truncated Gaussian elevation and also on mountain terrain data, namely, Long’s peak, for different noise levels. Performance comparisons show that iSpInPhase outperforms the state-of-the-art techniques in terms of standard performance assessment measures.