Identification of Statistically Homogeneous Pixels Based on One-Sample Test

Keng-Fan Lin,Daniele Perissin

doi:10.3390/rs9010037

Abstract

Statistically homogeneous pixels (SHP) play a crucial role in synthetic aperture radar (SAR) analysis. In past studies, various two-sample tests were applied on multitemporal SAR data stacks under the assumption of having stationary backscattering properties over time. In this letter, we propose the Robust T-test (TR) to improve the effectiveness of test operation. The TR test reduces the impact of temporal variabilities and outliers, thus helping to identify SHP with assurances of similar temporal behaviors. This method includes three steps: (1) signal suppression; (2) outlier removal; and (3) one-sample test. In the experiments, we apply the TR test on both simulated and real data. Different stack sizes, types of distributions, and hypothesis tests are compared. Results of both experiments signify that the TR test outperforms conventional approaches and provides reliable SHP for SAR image analysis.

Highlights

Homogeneous pixels (SHP) represent pixels that share similar statistical characteristics over time
In the Synthetic Aperture Radar (SAR) community, using a stack of images to identify Statistically homogeneous pixels (SHP) in space has been extensively used for various SAR applications, such as adaptive filtering [1,2,3,4,5,6] [7,8], complex coherence estimation [9,10,11,12], and image segmentation [13]
As processing images based on SHP precludes the participation of irrelevant observations, successful SHP identification plays a key role in the SAR

Summary

Introduction

Homogeneous pixels (SHP) represent pixels that share similar statistical characteristics over time. In the Synthetic Aperture Radar (SAR) community, using a stack of images to identify SHP in space has been extensively used for various SAR applications, such as adaptive filtering [1,2,3,4,5,6] (multitemporal configurations) [7,8] (single-temporal configurations), complex coherence estimation [9,10,11,12], and image segmentation [13]. A generic approach for SHP identification is through hypothesis tests. Pixels showing no significant differences from each other will be incorporated into the same SHP family. The hypothesis tests can be divided into two categories: nonparametric and parametric tests

Methods

Discussion

Conclusion