An optimization method for homogeneous pixels based on iterative confidence interval testing

Existing methods for homogeneous pixel selection often fail to balance omission (Type I) and commission (Type II) errors. While the Hypothesis Test of Confidence Interval (HTCI) is computationally efficient, its accuracy is compromised by sensitivity to initial estimates. This study introduces Iter-HTCI, an algorithm that enhances selection accuracy through iterative optimization of the reference estimate using Gamma distribution confidence intervals. Simulations and experiments demonstrate that Iter-HTCI achieves statistical power approaching theoretical maxima. Compared to the Baumgartner-Weiss-Schindler (BWS) and HTCI benchmarks, Iter-HTCI improves small-sample accuracy by 74.36% and 43.41%, respectively. Furthermore, it effectively mitigates over-rejection issues in weak scattering zones. Phase optimization metrics show significant enhancement, with Phase Standard Deviation and Sum of Phase Differences reduced by 31.46% and 58.09% compared to HTCI. Although processing time increases slightly relative to HTCI, Iter-HTCI remains significantly more efficient than BWS and delivers superior phase information for time-series deformation retrieval.