Polarimetric remote sensing system analysis: Digital Imaging and Remote Sensing Image Generation (DIRSIG) model validation and impact of polarization phenomenology on material discriminability

In addition to spectral information acquired by traditional multi/hyperspectral systems, passive electro optical and infrared (EO/IR) polarimetric sensors also measure the polarization response of different materials in the scene. Such an imaging modality can be useful in improving surface characterization; however, the characteristics of polarimetric systems have not been completely explored by the remote sensing community. Therefore, the main objective of this research was to advance our knowledge in polarimetric remote sensing by investigating the impact of polarization phenomenology on material discriminability. The first part of this research focuses on system validation, where the major goal was to assess the fidelity of the polarimetric images simulated using the Digital Imaging and Remote Sensing Image Generation (DIRSIG) model. A theoretical framework, based on polarization vision models used for animal vision studies and industrial defect detection applications, was developed within which the major components of the polarimetric image chain were validated. In the second part of this research, a polarization physics based approach for improved material discriminability was proposed. This approach utilizes the angular variation in the polarization response to infer the physical characteristics of the observed surface by imaging the scene in three different view directions. The usefulness of the proposed approach in improving detection performance in the absence of apriori knowledge about the target geometry was demonstrated. Sensitivity analysis of the proposed system for different scene related parameters was performed to identify the imaging conditions under which the material discriminability is maximized. Furthermore, the detection performance of the proposed polarimetric system was compared to that of the hyperspectral system to identify scenarios where polarization information can be very useful in improving the target contrast

Polarization Remote Sensing for Land Observation

In the real world, vegetation, liquid surfaces, rocks, buildings, snows, clouds, fogs, etc. can all be regarded as natural polarizers. In the process of reflecting, transmitting, and scattering of electromagnetic radiations, land surface objects can produce polarized features that are related to the nature of the materials. These polarized information can determine objects’ properties, and therefore, detecting the polarization information of objects becomes a new method of remote sensing. Polarization of reflected and scattered solar electromagnetic radiation adds a new dimension to the understanding of the Earth’s objects’ properties. The polarized bidirectional reflectance characteristics and polarized hyperspectral properties of land objects were methodically studied. The results of the polarized bidirectional reflectance characteristics can provide the theoretical basis for polarization remote sensing such as the detecting conditions, modeling and others. The polarized spectral property of the typical objects can be used as the spectral basis for polarization remote sensing. The atmospheric correction is a key problem when using polarization remote sensing method to detect land objects’ information, because scattered atmospheric particles exhibit stronger polarization phenomena than land objects do. A method of using atmospheric neutral point for the separation polarization effect between objects and atmosphere has been proposed

Neural Spectro-polarimetric Fields

Modeling the spatial radiance distribution of light rays in a scene has been extensively explored for applications, including view synthesis. Spectrum and polarization, the wave properties of light, are often neglected due to their integration into three RGB spectral bands and their non-perceptibility to human vision. Despite this, these properties encompass substantial material and geometric information about a scene. In this work, we propose to model spectro-polarimetric fields, the spatial Stokes-vector distribution of any light ray at an arbitrary wavelength. We present Neural Spectro-polarimetric Fields (NeSpoF), a neural representation that models the physically-valid Stokes vector at given continuous variables of position, direction, and wavelength. NeSpoF manages inherently noisy raw measurements, showcases memory efficiency, and preserves physically vital signals, factors that are crucial for representing the high-dimensional signal of a spectro-polarimetric field. To validate NeSpoF, we introduce the first multi-view hyperspectral-polarimetric image dataset, comprised of both synthetic and real-world scenes. These were captured using our compact hyperspectral-polarimetric imaging system, which has been calibrated for robustness against system imperfections. We demonstrate the capabilities of NeSpoF on diverse scenes

Modeling Atmosphere-Ocean Radiative Transfer: A PACE Mission Perspective

The research frontiers of radiative transfer (RT) in coupled atmosphere-ocean systems are explored to enable new science and specifically to support the upcoming Plankton, Aerosol, Cloud ocean Ecosystem (PACE) satellite mission. Given (i) the multitude of atmospheric and oceanic constituents at any given moment that each exhibits a large variety of physical and chemical properties and (ii) the diversity of light-matter interactions (scattering, absorption, and emission), tackling all outstanding RT aspects related to interpreting and/or simulating light reflected by atmosphere-ocean systems becomes impossible. Instead, we focus on both theoretical and experimental studies of RT topics important to the science threshold and goal questions of the PACE mission and the measurement capabilities of its instruments. We differentiate between (a) forward (FWD) RT studies that focus mainly on sensitivity to influencing variables and/or simulating data sets, and (b) inverse (INV) RT studies that also involve the retrieval of atmosphere and ocean parameters. Our topics cover (1) the ocean (i.e., water body): absorption and elastic/inelastic scattering by pure water (FWD RT) and models for scattering and absorption by particulates (FWD RT and INV RT); (2) the air-water interface: variations in ocean surface refractive index (INV RT) and in whitecap reflectance (INV RT); (3) the atmosphere: polarimetric and/or hyperspectral remote sensing of aerosols (INV RT) and of gases (FWD RT); and (4) atmosphere-ocean systems: benchmark comparisons, impact of the Earth's sphericity and adjacency effects on space-borne observations, and scattering in the ultraviolet regime (FWD RT). We provide for each topic a summary of past relevant (heritage) work, followed by a discussion (for unresolved questions) and RT updates

Modeling atmosphere-ocean radiative transfer: A PACE mission perspective

The research frontiers of radiative transfer (RT) in coupled atmosphere-ocean systems are explored to enable new science and specifically to support the upcoming Plankton, Aerosol, Cloud ocean Ecosystem (PACE) satellite mission. Given (i) the multitude of atmospheric and oceanic constituents at any given moment that each exhibits a large variety of physical and chemical properties and (ii) the diversity of light-matter interactions (scattering, absorption, and emission), tackling all outstanding RT aspects related to interpreting and/or simulating light reflected by atmosphere-ocean systems becomes impossible. Instead, we focus on both theoretical and experimental studies of RT topics important to the science threshold and goal questions of the PACE mission and the measurement capabilities of its instruments. We differentiate between (a) forward (FWD) RT studies that focus mainly on sensitivity to influencing variables and/or simulating data sets, and (b) inverse (INV) RT studies that also involve the retrieval of atmosphere and ocean parameters. Our topics cover (1) the ocean (i.e., water body): absorption and elastic/inelastic scattering by pure water (FWD RT) and models for scattering and absorption by particulates (FWD RT and INV RT); (2) the air-water interface: variations in ocean surface refractive index (INV RT) and in whitecap reflectance (INV RT); (3) the atmosphere: polarimetric and/or hyperspectral remote sensing of aerosols (INV RT) and of gases (FWD RT); and (4) atmosphere-ocean systems: benchmark comparisons, impact of the Earth’s sphericity and adjacency effects on space-borne observations, and scattering in the ultraviolet regime (FWD RT). We provide for each topic a summary of past relevant (heritage) work, followed by a discussion (for unresolved questions) and RT updates

Modeling Atmosphere-Ocean Radiative Transfer: A PACE Mission Perspective

The research frontiers of radiative transfer (RT) in coupled atmosphere-ocean systems are explored to enable new science and specifically to support the upcoming Plankton, Aerosol, Cloud ocean Ecosystem (PACE) satellite mission. Given (i) the multitude of atmospheric and oceanic constituents at any given moment that each exhibits a large variety of physical and chemical properties and (ii) the diversity of light-matter interactions (scattering, absorption, and emission), tackling all outstanding RT aspects related to interpreting and/or simulating light reflected by atmosphere-ocean systems becomes impossible. Instead, we focus on both theoretical and experimental studies of RT topics important to the science threshold and goal questions of the PACE mission and the measurement capabilities of its instruments. We differentiate between (a) forward (FWD) RT studies that focus mainly on sensitivity to influencing variables and/or simulating data sets, and (b) inverse (INV) RT studies that also involve the retrieval of atmosphere and ocean parameters. Our topics cover (1) the ocean (i.e., water body): absorption and elastic/inelastic scattering by pure water (FWD RT) and models for scattering and absorption by particulates (FWD RT and INV RT); (2) the air-water interface: variations in ocean surface refractive index (INV RT) and in whitecap reflectance (INV RT); (3) the atmosphere: polarimetric and/or hyperspectral remote sensing of aerosols (INV RT) and of gases (FWD RT); and (4) atmosphere-ocean systems: benchmark comparisons, impact of the Earth’s sphericity and adjacency effects on space-borne observations, and scattering in the ultraviolet regime (FWD RT). We provide for each topic a summary of past relevant (heritage) work, followed by a discussion (for unresolved questions) and RT updates

Analytical modeling, performance analysis, and optimization of polarimetric imaging system

Polarized light can provide additional information about a scene that cannot be obtained directly from intensity or spectral images. Rather than treating the optical field as scalar, polarization images seek to obtain the vector nature of the optical field from the scene. Polarimetry thus has been found to be useful in several applications, including material classification and target detection. Recently, optical polarization has been identified as an emerging technique and has shown promising applications in passive remote sensing. Compared with the traditional spectral content of the scene, polarimetric signatures are much more dependent on the scene geometry and the polarimetric bidirectional reflectance distribution function (pBRDF) of the objects. Passive polarimetric scene simulation has been shown to be helpful in better understanding such phenomenology. However, the combined effects of the scene characteristics, the sensor noise and optical imperfections, and the different processing algorithm implementations on the overall system performance have not been systematically studied. To better understand the effects of various system attributes and help optimize the design and use of polarimetric imaging system, an analytical model has been developed to predict the system performance. A detailed introduction of the analytical model is first presented. The model propagates the first and second order statistics of radiance from a scene model to a sensor model, and finally to a processing model. Validation with data collected from a division of time polarimeter show good agreement between model predictions and measurements. It has been shown that the analytical model is able to predict the general polarization behavior and data trends with different scene geometries. Based on the analytical model we then define several system performance metrics to evaluate the polarimetic signatures of different objects as well as target detection performance. Parameter tradeoff studies have been conducted for analysis of potential system performance. Finally based on the analytical model and system performance metrics we investigate optimal filter configurations to sense polarization. We develop an adaptive polarimetric target detector to determine the optimum analyzer orientations for a multichannel polarization-sensitive optical system. Compared with several conventional operation methods, we find that better target detection performance is achieved with our algorithm

Polarimetric remote sensing in oxygen A and B bands: sensitivity study and information content analysis for vertical profile of aerosols

Theoretical analysis is conducted to reveal the information content of aerosol vertical profile in space-borne measurements of the backscattered radiance and degree of linear polarization (DOLP) in oxygen (O2) A and B bands. Assuming a quasi-Gaussian shape for aerosol vertical profile characterized by peak height H and half width y (at half maximum), the Unified Linearized Vector Radiative Transfer Model (UNL-VRTM) is used to simulate the Stokes fourvector elements of upwelling radiation at the top of atmosphere (TOA) and their Jacobians with respect to H and y. Calculations for different aerosol types and different combinations of H and values show that the wide range of gas absorption optical depth in O2 A and B band enables the sensitivity of backscattered DOLP and radiance at TOA to the aerosol layer at different altitudes. Quantitatively, DOLP in O2 A and B bands is found to be more sensitive to H and y than radiance, especially over the bright surfaces (with large visible reflectance). In many O2 absorption wavelengths, the degree of freedom of signal (DFS) for retrieving H (or y) generally increases with H (and y) and can be close to unity in many cases, assuming that the composite uncertainty from surface and aerosol scattering properties as well as measurements is less than 5%. Further analysis demonstrates that DFS needed for simultaneous retrieval of H and y can be obtained from a combined use of DOLP measurements at ~10–100 O2 A and B absorption wavelengths (or channels), depending on the specific values of H. The higher the aerosol layer, the fewer number of channels for DOLP measurements in O2 A and B bands are needed for characterizing H and . Future hyperspectral measurements of DOLP in O2 A and B bands are needed to continue studying their potential and their combination with radiance and DOLP in atmospheric window channels for retrieving the vertical profiles of aerosols, especially highly scattering aerosols, over land

Incorporation of Polarization Into the DIRSIG Synthetic Image Generation Model

The Digital Imaging and Remote Sensing Synthetic Image Generation (DIRSIG) model uses a quantitative first principles approach to generate synthetic hyperspectral imagery. This paper presents the methods used to add modeling of polarization phenomenology. The radiative transfer equations were modified to use Stokes vectors for the radiance values and Mueller matrices for the energy-matter interactions. The use of Stokes vectors enables a full polarimetric characterization of the illumination and sensor reaching radiances. The bi-directional reflectance distribution function (BRDF) module was rewritten and modularized to accommodate a variety of polarized and unpolarized BRDF models. Two new BRDF models based on Torrance- Sparrow and Beard-Maxwell were added to provide polarized BRDF estimations. The sensor polarization characteristics are modeled using Mueller matrix transformations on a per pixel basis. All polarized radiative transfer calculations are performed spectrally to preserve the hyperspectral capabilities of DIRSIG. Integration over sensor bandpasses is handled by the sensor module