Radar imaging

Function (biology)

Algorithm

Mutual coherence

Biology

Statistics

Coherence (philosophical gambling strategy)

Interferometric synthetic aperture radar

Telecommunications

Estimation theory

Data mining

Computer science

Mathematical optimization

Geology

Evolutionary biology

Paleontology

Physics

Likelihood function

Mathematics

Optics

Geography

Synthetic aperture radar

Interferometry

Artificial intelligence

Remote sensing

Series (stratigraphy)

Measure (data warehouse)

Radar

IEEE Transactions on Geoscience and Remote Sensing

The proper use of distributed scatterer (DS) can improve both the density and quality of synthetic aperture radar (SAR) interferometry (InSAR) measurements. A critical step in DS interferometry (DSI) is the restoration of a consistent phase series from SAR interferogram stacks. Most state-of-the-art algorithms adopt an approximate likelihood function to calculate the likelihood by replacing the true coherence matrix with its estimation, more specifically, the sample coherence matrix (SCM). However, this approximation has a drawback in that the coherence estimates are greatly biased when the coherence is low. In this study, we derive a new likelihood function without such an approximation. Accordingly, a DSI framework using this function for phase estimation and point selection is provided. In this framework, the new likelihood function serves as a cost function for phase estimation and a quality measure for DS selection. Its performance is investigated by experiments in a simulation study and a real-world case study using Sentinel-1 data over Shenzhen airport in China. The results reveal that the proposed DSI framework outperforms the existing state-of-the-art approaches in different scenarios, in terms of providing a more accurate estimation and improving DS density and coverage.

A New Likelihood Function for Consistent Phase Series Estimation in Distributed Scatterer Interferometry