Signal-to-Noise Ratio Model and Imaging Performance Analysis of Photonic Integrated Interferometric System for Remote Sensing

Photonic integrated interferometric imaging systems (PIISs) provide a compact solution for high-resolution Earth observation missions with stringent size, weight, and power (SWaP) constraints. As an indirect imaging method, a PIIS exhibits fundamentally different noise response characteristics compared to conventional remote sensing systems, and its imaging performance under practical operational scenarios has not been thoroughly investigated. The primary objective of this paper is to evaluate the operational capabilities of PIISs under remote sensing conditions. We (1) establish a signal-to-noise-ratio model for PIISs with balanced four-quadrature detection, (2) analyze the impacts of intensity noise and turbulent phase noise based on radiative transfer and turbulence models, and (3) simulate imaging performance with WorldView-3-like parameters. The results of the visibility signal-to-noise ratio (SNR) analysis demonstrate that the system’s minimum detectable fringe visibility is inversely proportional to the reciprocal of the sub-aperture intensity signal-to-noise ratio. When the integration time reaches 100 ms, the minimum detectable fringe visibility ranges between 10−2 and 10−3 (at 10 dB system efficiency). Imaging simulations demonstrate that recognizable image reconstruction requires integration times exceeding 10 ms for 10 cm baselines, achieving approximately 25 dB PSNR and 0.8 SSIM at 100 ms integration duration. These results may provide references for potential applications of photonic integrated interferometric imaging systems in remote sensing.

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Publication Details

Journal
Remote Sensing
Published
2025-04-22
DOI
https://doi.org/10.3390/rs17091484
Primary Topic
Adaptive optics and wavefront sensing
Type
article
Field-Weighted Citation Impact
0.00

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article

Signal-to-Noise Ratio Model and Imaging Performance Analysis of Photonic Integrated Interferometric System for Remote Sensing

Chuang Zhang, Yan He, Qinghua Yu
Remote Sensing
Adaptive optics and wavefront sensing
article

Signal-to-Noise Ratio Model and Imaging Performance Analysis of Photonic Integrated Interferometric System for Remote Sensing

Chuang Zhang, Yan He, Qinghua Yu
article en

Abstract

Photonic integrated interferometric imaging systems (PIISs) provide a compact solution for high-resolution Earth observation missions with stringent size, weight, and power (SWaP) constraints. As an indirect imaging method, a PIIS exhibits fundamentally different noise response characteristics compared to conventional remote sensing systems, and its imaging performance under practical operational scenarios has not been thoroughly investigated. The primary objective of this paper is to evaluate the operational capabilities of PIISs under remote sensing conditions. We (1) establish a signal-to-noise-ratio model for PIISs with balanced four-quadrature detection, (2) analyze the impacts of intensity noise and turbulent phase noise based on radiative transfer and turbulence models, and (3) simulate imaging performance with WorldView-3-like parameters. The results of the visibility signal-to-noise ratio (SNR) analysis demonstrate that the system’s minimum detectable fringe visibility is inversely proportional to the reciprocal of the sub-aperture intensity signal-to-noise ratio. When the integration time reaches 100 ms, the minimum detectable fringe visibility ranges between 10−2 and 10−3 (at 10 dB system efficiency). Imaging simulations demonstrate that recognizable image reconstruction requires integration times exceeding 10 ms for 10 cm baselines, achieving approximately 25 dB PSNR and 0.8 SSIM at 100 ms integration duration. These results may provide references for potential applications of photonic integrated interferometric imaging systems in remote sensing.

Remote SensingVol. 17(9)
Shanghai Institute of Technical Physics (CN), University of Chinese Academy of Sciences (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 94%
Adaptive optics and wavefront sensing
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