Statistical scene generation for polarimetric imaging systems

TL;DR

This paper introduces a framework for simulating temporally correlated polarimetric image data with power law spectral properties, aiding in the design and testing of polarimetric imaging systems.

Contribution

It presents a novel method and code for generating synthetic polarimetric image sequences with realistic statistical and temporal correlations.

Findings

Generated data follows power law spectral distributions.

Simulated data exhibits temporal spectral correlations.

Framework supports testing of polarimetric system performance.

Abstract

Little publicly available data exists for polarimetric measurements. When designing task specific polarimetric systems, the statistical properties of the task specific data becomes important. Until better polarimetric datasets are available to deduce statistics from, the statistics must be simulated to test instrument performance. Most imaged scenes have been shown to follow a power law power spectral density distribution, for both natural and city scenes. Furthermore, imaged data appears to follow a power law power spectral distribution temporally. We are interested in generating image sets which change over time, and at the same time are correlated between different components (spectral or polarimetric). In this brief communication, we present a framework and provide code to generate such data.