Vimal Thilak

Polarization-based index of refraction and reflection angle estimation for remote sensing applications

A passive-polarization-based imaging system records the polarization state of light reflected by objects that are illuminated with an unpolarized and generally uncontrolled source. Such systems can be useful in many remote sensing applications including target detection, object segmentation, and material classification. We present a method to jointly estimate the complex index of refraction and the reflection angle (reflected zenith angle) of a target from multiple measurements collected by a passive polarimeter. An expression for the degree of polarization is derived from the microfacet polarimetric bidirectional reflectance model for the case of scattering in the plane of incidence. Using this expression, we develop a nonlinear least-squares estimation algorithm for extracting an apparent index of refraction and the reflection angle from a set of polarization measurements collected from multiple source positions. Computer simulation results show that the estimation accuracy generally improves with an increasing number of source position measurements. Laboratory results indicate that the proposed method is effective for recovering the reflection angle and that the estimated index of refraction provides a feature vector that is robust to the reflection angle.

Image Segmentation from Multi-look Passive Polarimetric Imagery

A passive imaging polarimeter records the polarization state of light reflected by an object that is illuminated with an unpolarized and usually uncontrolled source. Passive polarimetric imagery has shown to be useful in many remote sensing applications including shape extraction, material classification and target detection/recognition. In this paper, we present an image segmentation algorithm that automatically extracts an object from multi-look passive polarimetric imagery. The term multi-look refers to multiple polarization measurements where the position of the source of illumination (typically the Sun in passive systems) changes between measurements. The proposed method relies on our previous work on estimating the complex index of refraction and reflection angle from multi-look passive polarimetric imagery. We experimentally showed that the estimates for the index of refraction were largely invariant to both the position of the source and the view angle. Consequently, we utilize the index of refraction as a feature vector to design an illumination invariant image segmentation algorithm. A clustering approach based on the classic c-means algorithm is used for segmenting objects based on their index of refraction. The proposed segmentation approach is validated by using data collected under laboratory conditions. Experimental results indicate that the proposed method is effective for segmenting various targets of interest.

Estimating the Complex Index of Refraction and View Angle of an Object using Multiple Polarization Measurements

A passive polarization based imaging system records the polarization state of light reflected by objects that are illuminated with an unpolarized and generally uncontrolled source. Such systems can be useful in many remote sensing applications including target detection, object segmentation and material classification. In this paper we present a method to jointly estimate the complex index of refraction and the view angle of a target from multiple measurements collected by a passive polarimeter. This generalizes our previous work which was applicable only to dielectric targets. An expression for the degree of polarization is derived from the microfacet polarimetric bidirectional reflectance model for the case of scattering in the place of incidence. Using this expression, we develop nonlinear least squares estimation algorithms for extracting the complex index of refraction and view angle from multiple polarization measurements. The effectiveness of the proposed method is validated with data collected in laboratory conditions. Experimental results indicate that the proposed method is effective for recovering the parameters of interest for real world data and that the complex index of refraction thus computed provides a feature vector that is robust to the view angle.

Material Classification using Passive Polarimetric Imagery

Passive imaging polarimetry has emerged as an useful tool in many remote sensing applications including material classification, target detection and shape extraction. In this paper we present a method to classify specular objects based on their material composition from passive polarimetric imagery. The proposed algorithm is built on an iterative model-based method to recover the complex index of refraction of a specular target from multiple polarization measurements. The recovered parameters are then used to discriminate between objects by employing the nearest neighbor rule. The effectiveness of the proposed method is validated with data collected in laboratory conditions. Experimental results indicate that the classification approach is highly effective for distinguishing between various targets of interest. Most significantly, the proposed classification method is robust to a wide range of observational geometry.

Active Polarimetric Imaging for Estimation of Scene Geometry

Active imaging polarimetry for remote sensing applications has received significant attention recently. Such systems use a variably-polarized active light source to illuminate target objects. Multiple images with different polarizations are then captured and used to build Stokes vectors which are, in turn, used to estimate the refraction indices of materials as well as the relative geometry of the target object. The applications facilitated by active polarimetry include target detection, object recognition, shape extraction and material classification. Unfortunately, this estimation problem requires us to find the solution to a system of nonlinear equations using an iterative optimization technique. In this paper, we introduce a methodology for finding and validating good solutions to this optimization.

Passive Polarimetric Imagery Based Material Classification For Remote Sensing Applications

Passive imaging polarimetry has emerged as a useful tool in many remote sensing applications including material classification, target detection and shape extraction. In this paper we present a method to classify specular objects based on their material composition from passive polarimetric imagery. The proposed algorithm is built on an iterative, model-based method to recover the complex index of refraction of a specular target from multiple polarization measurements. The recovered parameters are then used to discriminate between objects by employing the nearest neighbor rule. Experimental results indicate that the classification approach is highly effective for distinguishing between various targets of interest. Most significantly, the proposed classification method is robust to a wide range of observational geometry.

Estimation of target geometry from Mueller matrix imagery

An imaging polarimeter records the polarization state of light reflected by an object that is illuminated with a polarized source such as a laser. Active polarimetric imagery has been shown to be useful in many remote sensing applications including shape extraction, material classification and target detection/recognition. In this paper, we present a method that automatically extracts the angle of incidence, angle of reflection and the relative azimuthal angle from Mueller matrix imagery. Mueller matrix imagery provides multiple measurements from which we can construct a nonlinear system of equations. This system is solved using the Levenberg-Marquardt algorithm which is a standard nonlinear equation solver. We experimentally demonstrate via computer simulations that the parameter estimates can be estimated accurately using our approach.

Estimation of incidence and reflection angles from passive polarimetric imagery: extension to out-of-plane scattering

Passive polarimetric imagery conveys information that complements the information contained in intensity and spectral imagery. Passive polarimetric measurements have been exploited in many remote sensing applications such as shape extraction, surface inspection and object detection/recognition. In previous work Thilak et al. proposed an algorithm to estimate the index of refraction and view angle (object surface orientation) from multiple polarization images where the source position changes between measurements. That work relies on a specular polarimetric bidirectional reflectance distribution function (pBRDF) developed by Priest and Meier. The pBRDF incorporates a Mueller matrix that characterizes the polarized reflection properties of a target for any incident Stokes vector. The results in Thilak et al. assumed that scattering occurs in the plane of incidence, which means that the pBRDF matrix contains many zero elements. In this paper, we extend this work to an out-of-plane scattering geometry, which implies that the pBRDF matrix contains more non-zero elements. In the initial work presented here, a nonlinear optimization approach is utilized to estimate the incident and reflection angles from a single polarization measurement assuming knowledge of the surface index of refraction and azimuthal angle between source and receiver. The effectiveness of the proposed method is verified through computer simulation.

Estimating the refractive index and reflected zenith angle of a target using multiple polarization measurements

A passive polarization based imaging system records the polarization state of light reflected by objects that are illuminated with an unpolarized and generally uncontrolled source. The information conveyed by the polarization state of light has been exploited in applications such as target detection, shape extraction and material classification. In this paper we present a method to jointly estimate the refractive index and the reflected zenith angle from two measurements collected by a passive polarimeter. An expression for the degree of polarization is derived from the microfacet polarimetric bidirectional reflectance model for the case of scattering in the place of incidence. The parameters of interest are iteratively estimated from polarization measurements assumed to be collected with a polarimeter. Computer simulations are presented to demonstrate the effectiveness of the proposed method.

Pattern recognition for passive polarimetric data using nonparametric classifiers

Passive polarization based imaging is a useful tool in computer vision and pattern recognition. A passive polarization imaging system forms a polarimetric image from the reflection of ambient light that contains useful information for computer vision tasks such as object detection (classification) and recognition. Applications of polarization based pattern recognition include material classification and automatic shape recognition. In this paper, we present two target detection algorithms for images captured by a passive polarimetric imaging system. The proposed detection algorithms are based on Bayesian decision theory. In these approaches, an object can belong to one of any given number classes and classification involves making decisions that minimize the average probability of making incorrect decisions. This minimum is achieved by assigning an object to the class that maximizes the a posteriori probability. Computing a posteriori probabilities requires estimates of class conditional probability density functions (likelihoods) and prior probabilities. A Probabilistic neural network (PNN), which is a nonparametric method that can compute Bayes optimal boundaries, and a -nearest neighbor (KNN) classifier, is used for density estimation and classification. The proposed algorithms are applied to polarimetric image data gathered in the laboratory with a liquid crystal-based system. The experimental results validate the effectiveness of the above algorithms for target detection from polarimetric data.