2014Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIERequires access

Radiometric sensitivity contrast metrics for hyperspectral remote sensors

John F. Silny, Lou Zellinger

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Abstract

This paper discusses the calculation, interpretation, and implications of various radiometric sensitivity metrics for Earth-observing hyperspectral imaging (HSI) sensors. The most commonly used sensor performance metric is signal-to-noise ratio (SNR), from which additional noise equivalent quantities can be computed, including: noise equivalent spectral radiance (NESR), noise equivalent delta reflectance (NEΔρ), noise equivalent delta emittance (NEΔƐ), and noise equivalent delta temperature (NEΔT). For hyperspectral sensors, these metrics are typically calculated from an at-aperture radiance (typically generated by MODTRAN) that includes both target radiance and non-target (atmosphere and background) radiance. Unfortunately, these calculations treat the entire at-aperture radiance as the desired signal, even when the target radiance is only a fraction of the total (such as when sensing through a long or optically dense atmospheric path). To overcome this limitation, an augmented set of metrics based on contrast signal-to-noise ratio (CNSR) is developed, including their noise equivalent counterparts (CNESR, CNEΔρ, CNEΔƐ, and CNEΔT). These contrast metrics better quantify sensor performance in an operational environment that includes remote sensing through the atmosphere.

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What this paper is about

This paper discusses the calculation, interpretation, and implications of various radiometric sensitivity metrics for Earth-observing hyperspectral imaging (HSI) sensors. The most commonly used sensor performance metric is signal-to-noise ratio (SNR), from which additional noise equivalent quantities can be computed, including: noise equivalent spectral radiance (NESR), noise equivalent delta reflectance (NEΔρ), noise equivalent delta emittance (NEΔƐ), and noise equivalent delta temperature (NEΔT). For hyperspectral sensors, these metrics are typically calculated from an at-aperture radiance (typically generated by MODTRAN) that includes both target radiance and non-target (atmosphere and background) radiance. Unfortunately, these calculations treat the entire at-aperture radiance as the desired signal, even when the target radiance is only a fraction of the total (such as when sensing through a long or optically dense atmospheric path). To overcome this limitation, an augmented set of metrics based on contrast signal-to-noise ratio (CNSR) is developed, including their noise equivalent counterparts (CNESR, CNEΔρ, CNEΔƐ, and CNEΔT). These contrast metrics better quantify sensor performance in an operational environment that includes remote sensing through the atmosphere.

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Available abstract

This paper discusses the calculation, interpretation, and implications of various radiometric sensitivity metrics for Earth-observing hyperspectral imaging (HSI) sensors. The most commonly used sensor performance metric is signal-to-noise ratio (SNR), from which additional noise equivalent quantities can be computed, including: noise equivalent spectral radiance (NESR), noise equivalent delta reflectance (NEΔρ), noise equivalent delta emittance (NEΔƐ), and noise equivalent delta temperature (NEΔT). For hyperspectral sensors, these metrics are typically calculated from an at-aperture radiance (typically generated by MODTRAN) that includes both target radiance and non-target (atmosphere and background) radiance. Unfortunately, these calculations treat the entire at-aperture radiance as the desired signal, even when the target radiance is only a fraction of the total (such as when sensing through a long or optically dense atmospheric path). To overcome this limitation, an augmented set of metrics based on contrast signal-to-noise ratio (CNSR) is developed, including their noise equivalent counterparts (CNESR, CNEΔρ, CNEΔƐ, and CNEΔT). These contrast metrics better quantify sensor performance in an operational environment that includes remote sensing through the atmosphere.

Key concepts: Hyperspectral imaging, Remote sensing, Contrast (vision), Radiometric dating, Sensitivity (control systems), Computer science, Radiometry, Environmental science

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