Estimating the polarization degree of polarimetric images in coherent illumination using maximum likelihood methods

This paper addresses the problem of estimating the polarization degree of polarimetric images in coherent illumination. It has been recently shown that the degree of polarization associated to polarimetric images can be estimated by the method of moments applied to two or four images assuming fully developed speckle. This paper shows that the estimation can also be conducted by using maximum likelihood methods. The maximum likelihood estimators of the polarization degree are derived from the joint distribution of the image intensities. We show that the joint distribution of polarimetric images is a multivariate gamma distribution whose marginals are univariate, bivariate or trivariate gamma distributions. This property is used to derive maximum likelihood estimators of the polarization degree using two, three or four images. The proposed estimators provide better performance that the estimators of moments. These results are illustrated by estimations conducted on synthetic and real images

POL-LWIR Vehicle Detection: Convolutional Neural Networks Meet Polarised Infrared Sensors

For vehicle autonomy, driver assistance and situational awareness, it is necessary to operate at day and night, and in all weather conditions. In particular, long wave infrared (LWIR) sensors that receive predominantly emitted radiation have the capability to operate at night as well as during the day. In this work, we employ a polarised LWIR (POL-LWIR) camera to acquire data from a mobile vehicle, to compare and contrast four different convolutional neural network (CNN) configurations to detect other vehicles in video sequences. We evaluate two distinct and promising approaches, two-stage detection (Faster-RCNN) and one-stage detection (SSD), in four different configurations. We also employ two different image decompositions: the first based on the polarisation ellipse and the second on the Stokes parameters themselves. To evaluate our approach, the experimental trials were quantified by mean average precision (mAP) and processing time, showing a clear trade-off between the two factors. For example, the best mAP result of 80.94% was achieved using Faster-RCNN, but at a frame rate of 6.4 fps. In contrast, MobileNet SSD achieved only 64.51% mAP, but at 53.4 fps.Comment: Computer Vision and Pattern Recognition Workshop 201

Binary Classification of an Unknown Object through Atmospheric Turbulence Using a Polarimetric Blind-Deconvolution Algorithm Augmented with Adaptive Degree of Linear Polarization Priors

This research develops an enhanced material-classification algorithm to discriminate between metals and dielectrics using passive polarimetric imagery degraded by atmospheric turbulence. To improve the performance of the existing technique for near-normal collection geometries, the proposed algorithm adaptively updates the degree of linear polarization (DoLP) priors as more information becomes available about the scene. Three adaptive approaches are presented. The higher-order super-Gaussian method fits the distribution of DoLP estimates with a sum of two super-Gaussian functions to update the priors. The Gaussian method computes the classification threshold value, from which the priors are updated, by fitting the distribution of DoLP estimates with a sum of two Gaussian functions. Lastly, the distribution-averaging method approximates the threshold value by finding the mean of the DoLP distribution. The experimental results confirm that the new adaptive method significantly extends the collection geometry range of validity for the existing technique

Material Characterization Using Passive Multispectral Polarimetric Imagery

A new method for characterization of unknown targets using passive multispectral polarimetric imagery is presented. Previous work makes use of a pBRDF derived equation for the degree of linear polarization and with the aid of measurements at multiple incident angles estimates refractive index and reflection angle. This work uses known incident and reflection angles along with dispersion equations and polarimetric data at multiple wavelengths to recover the index of refraction. Although imagery is collected with a division of time polarimeter and a spectral filter wheel in iterative, manual steps, the new algorithm could be applied to any set of registered multispectral polarimetric images most notably those produced by a recently introduced division of focal plane multispectral polarimetric sensor. Experimental results are presented showing the novel algorithm\u27s ability to classify and characterize a range of materials

Analysis of Polarimetric Synthetic Aperture Radar and Passive Visible Light Polarimetric Imaging Data Fusion for Remote Sensing Applications

The recent launch of spaceborne (TerraSAR-X, RADARSAT-2, ALOS-PALSAR, RISAT) and airborne (SIRC, AIRSAR, UAVSAR, PISAR) polarimetric radar sensors, with capability of imaging through day and night in almost all weather conditions, has made polarimetric synthetic aperture radar (PolSAR) image interpretation and analysis an active area of research. PolSAR image classification is sensitive to object orientation and scattering properties. In recent years, significant work has been done in many areas including agriculture, forestry, oceanography, geology, terrain analysis. Visible light passive polarimetric imaging has also emerged as a powerful tool in remote sensing for enhanced information extraction. The intensity image provides information on materials in the scene while polarization measurements capture surface features, roughness, and shading, often uncorrelated with the intensity image. Advantages of visible light polarimetric imaging include high dynamic range of polarimetric signatures and being comparatively straightforward to build and calibrate. This research is about characterization and analysis of the basic scattering mechanisms for information fusion between PolSAR and passive visible light polarimetric imaging. Relationships between these two modes of imaging are established using laboratory measurements and image simulations using the Digital Image and Remote Sensing Image Generation (DIRSIG) tool. A novel low cost laboratory based S-band (2.4GHz) PolSAR instrument is developed that is capable of capturing 4 channel fully polarimetric SAR image data. Simple radar targets are formed and system calibration is performed in terms of radar cross-section. Experimental measurements are done using combination of the PolSAR instrument with visible light polarimetric imager for scenes capturing basic scattering mechanisms for phenomenology studies. The three major scattering mechanisms studied in this research include single, double and multiple bounce. Single bounce occurs from flat surfaces like lakes, rivers, bare soil, and oceans. Double bounce can be observed from two adjacent surfaces where one horizontal flat surface is near a vertical surface such as buildings and other vertical structures. Randomly oriented scatters in homogeneous media produce a multiple bounce scattering effect which occurs in forest canopies and vegetated areas. Relationships between Pauli color components from PolSAR and Degree of Linear Polarization (DOLP) from passive visible light polarimetric imaging are established using real measurements. Results show higher values of the red channel in Pauli color image (|HH-VV|) correspond to high DOLP from double bounce effect. A novel information fusion technique is applied to combine information from the two modes. In this research, it is demonstrated that the Degree of Linear Polarization (DOLP) from passive visible light polarimetric imaging can be used for separation of the classes in terms of scattering mechanisms from the PolSAR data. The separation of these three classes in terms of the scattering mechanisms has its application in the area of land cover classification and anomaly detection. The fusion of information from these particular two modes of imaging, i.e. PolSAR and passive visible light polarimetric imaging, is a largely unexplored area in remote sensing and the main challenge in this research is to identify areas and scenarios where information fusion between the two modes is advantageous for separation of the classes in terms of scattering mechanisms relative to separation achieved with only PolSAR

Estimating Index of Refraction from Polarimetric Hyperspectral Imaging Measurements

Current material identification techniques rely on estimating reflectivity or emissivity which vary with viewing angle. As off-nadir remote sensing platforms become increasingly prevalent, techniques robust to changing viewing geometries are desired. A technique leveraging polarimetric hyperspectral imaging (P-HSI), to estimate complex index of refraction, N̂(ν̃), an inherent material property, is presented. The imaginary component of N̂(ν̃) is modeled using a small number of “knot” points and interpolation at in-between frequencies ν̃. The real component is derived via the Kramers-Kronig relationship. P-HSI measurements of blackbody radiation scattered off of a smooth quartz window show that N̂(ν̃) can be retrieved to within 0.08 RMS error between 875 cm−1 ≤ ν̃ ≤ 1250 cm−1. P-HSI emission measurements of a heated smooth Pyrex beaker also enable successful N̂(ν̃) estimates, which are also invariant to object temperature

An Analysis of multimodal sensor fusion for target detection in an urban environment

This work makes a compelling case for simulation as an attractive tool in designing cutting-edge remote sensing systems to generate the sheer volume of data required for a reasonable trade study. The generalized approach presented here allows multimodal system designers to tailor target and sensor parameters for their particular scenarios of interest via synthetic image generation tools, ensuring that resources are best allocated while sensors are still in the design phase. Additionally, sensor operators can use the customizable process showcased here to optimize image collection parameters for existing sensors. In the remote sensing community, polarimetric capabilities are often seen as a tool without a widely accepted mission. This study proposes incorporating a polarimetric and spectral sensor in a multimodal architecture to improve target detection performance in an urban environment. Two novel multimodal fusion algorithms are proposed--one for the pixel level, and another for the decision level. A synthetic urban scene is rendered for 355 unique combinations of illumination condition and sensor viewing geometry with the Digital Imaging and Remote Sensing Image Generation (DIRSIG) model, and then validated to ensure the presence of enough background clutter. The utility of polarimetric information is shown to vary with the sun-target-sensor geometry, and the decision fusion algorithm is shown to generally outperform the pixel fusion algorithm. The results essentially suggest that polarimetric information may be leveraged to restore the capabilities of a spectral sensor if forced to image under less than ideal circumstances

A Qualitative Study on Microwave Remote Sensing and Challenges Faced in India

Over the past few decades remote sensing has expanded its limits with exponential rise in technology that facilitates accurate data fetching in real time. In view of some of the major problems faced by developing nations, particularly India with its recent advancement in space technology, remote sensing has a vital role to play in resolving many such problems. In the light of recent Global Space Programs where several satellites have been launched for large area mapping using microwave sensors, microwave remote sensing can play a vital role as India experiences a large number of disasters every year. Also, majority of Indian population relies on farming for their livelihood. Microwave remote sensing can have significant effects in both these two scenarios as opposed to its conventional counterpart, optical remote sensing under diverse conditions and facilitate better results in terms of disaster management, prediction and increasing crop yield. The current paper brings out the various details on the work done by using active microwave remote sensing, with specific illustrative examples, for disaster management support, crop management techniques and the challenges associated on carrying out such researches in a diverse terrain like India

Study of Seasonal change and Water Stress Condition in Plant Leaf Using Polarimetric Lidar Measurement

Study of vegetation is of great importance to the improvement of agriculture and forest management. Although there have been various attempts to characterize vegetation using remote sensing techniques, polarimetric lidar is a novel remote sensing tool that has shown potential in vegetation remote sensing. In this thesis, a near-infrared polarimetric lidar at 1064 nm was used to investigate the effects of seasonal change and water stress condition on plant leaves. Two variables, time and water content, affected the plant leaf laser depolarization ratio measurement. The first study focused on the maple tree in order to figure out how seasonal change affected the maple leaf depolarization. Seasonal trendline was obtained and revealed an overall downward trend over time. In the second study, the leaves from maple, lemon, and rubber trees were investigated to study the effect of water stress on the depolarization ratio. It was discovered that the leaf depolarization ratio increased for more water content and went down for less water content. In addition, leaf samples were collected in the morning, afternoon, and evening, respectively, to study the diurnal change. Statistical analysis suggested that depolarization ratio did not change significantly for the different times of a day. It was suggested that the seasonal change had a greater effect on depolarization than the diurnal change. This study demonstrates that the near-infrared polarimetric lidar system has an ability to remotely characterize the vegetation internal conditions that may not be visible to the human eyes. Furthermore, the lidar system has the potential to differentiate the various plant species based on the depolarization ratio. In conclusion, the polarimetric lidar system at 1064-nm is an effective and sensitive enough remote sensing tool which can be widely used in active remote sensing