Chris Roussi

Microwave Backscatter From Arctic Lake Ice and Polarimetric Implications

Polarimetric synthetic aperture radar satellite and ground-based Ku- and X-band scatterometer measurements are used to explore the scattering mechanism for ice in shallow Arctic lakes, wherein strong radiometric responses are seen for floating ice, and low returns are evident where the ice has grounded. Scatterometer measurements confirm that high backscatter is from the ice/water interface, whereas polarimetric decomposition suggests that the dominant scattering mechanism from that interface is single bounce. Using Fresnel equations, a simple model for surface bounce from the ice/water interface is proposed, and its predictions are supported by experimental parameters such as co-pol phase difference, co-pol ratio, and the results of rigorous numerical modeling. Despite early research suggesting double-bounce scattering from columnar air bubbles and the ice/water interface as the dominant scattering mechanism in shallow lakes, this paper strongly supports a single-bounce model.

Developing an unpaved road assessment system for practical deployment with high-resolution optical data collection using a helicopter UAV

The need of local governments and transportation agencies to periodically asses the condition of unpaved roads in a cost-effective manner with rapid response times has lead to interest in the use of UAVs (Unmanned Aerial Vehicles) and remote sensing technologies. Currently these assessments are done through visual inspections with agency staff making occasional spot measurements. An unpaved road assessment system was developed to address these issues while at the same time providing a more accurate means of characterizing distresses and determining the roads condition for inspectors. This system uses a single-rotor UAV with a Digital Single-lens Reflex (DSLR) camera to capture overlapping imagery of unpaved roads. The UAV is equipped with a full combination GPS plus IMU (Inertial Measurement Unit) that allows it to fly predetermined waypoints with great stability while at the same time allowing the pilot the ability to take over at any time. Collected imagery is analyzed to locate road distresses. The imagery is run through a Structure From Motion (SfM) algorithm that generates a 3D model of the road surface from which additional condition information can be characterized. This system is easily transported and rapidly deployable to sections of unpaved roads for assessment.

Collecting Decision Support System Data Through Remote Sensing of Unpaved Roads

Unpaved roads make up roughly 33% of the road system within the United States and are vitally important to rural communities for transport of people and goods. Effective asset management of unpaved roads requires frequent inspections to determine the roads’ condition and the appropriate preventive maintenance or rehabilitation. The major challenge with managing unpaved roads is low-cost collection of condition data that are compatible with a decision support system (DSS). The advent of cheap, reliable remote-sensing platforms such as unmanned aerial vehicles along with the development of commercial off-the-shelf image analysis algorithms provides a revolutionary opportunity to overcome these data volume and efficiency issues. By taking advantage of these technological leaps, a market-ready system to detect unpaved road distress data compatible with a DSS was developed. The system uses aerial imagery that can be collected from a remote-controlled helicopter or manned fixed-wing aircraft to create a three-dimensional model of sensed road segments. Condition information on potholes, ruts, washboarding, loss of crown, and float aggregate berms is then detected and characterized to determine the extent and severity of the distress. Once detection and analysis are complete, the data are imported into a DSS based on a geographic information system (Road-soft) for use by road managers to prioritize preventive maintenance and rehabilitation efforts.

System models for IR diffractive optical systems based on a coherence theoretic framework

Diffractive optical systems in the Infrared (IR) wavelength regime are being re-examined for remote sensing applications. A pupil-plane adaptive coded aperture can enable a fine resolution, wide field of view sensor system without mechanical scanning. Due to the relatively long wavelengths, coded aperture systems in the IR have unique issues in regards to e.g. X-ray coded apertures. These include diffraction effects, wavelength dependence of optical elements, off axis aberrations due to thick screens, etc. In this paper, we provide a general system model framework based on partial coherence theory that enables us to explore many of the technical challenges in IR diffractive imaging. This paper develops the general theory and shows examples of issues that impact the optical transfer function (OTF) and impulse response of these types of architectures.