Browsing by Subject "Infrared"

Recent recognition models for remote sensing data (e.g., infrared cameras) are based upon machine learning models such as deep neural networks (DNNs) and typically require large quantities of labeled training data. However, many applications in remote sensing suffer from limited quantities of training data. To address this problem, we explore style transfer methods to leverage preexisting large and diverse datasets in more data-abundant sensing modalities (e.g., color imagery) so that they can be used to train recognition models on data-scarce target tasks. We first explore the potential efficacy of style transfer in the context of Buried Threat Detection using ground penetrating radar data. Based upon this work we found that simple pre-processing of downward-looking GPR makes it suitable to train machine learning models that are effective at recognizing threats in hand-held GPR. We then explore cross modal style transfer (CMST) for color-to-infrared stylization. We evaluate six contemporary CMST methods on four publicly-available IR datasets, the first comparison of its kind. Our analysis reveals that existing data-driven methods are either too simplistic or introduce significant artifacts into the imagery. To overcome these limitations, we propose meta-learning style transfer (MLST), which learns a stylization by composing and tuning well-behaved analytic functions. We find that MLST leads to more complex stylizations without introducing significant image artifacts and achieves the best overall performance on our benchmark datasets.

Forward Looking Infrared (FLIR) cameras have recently been studied as a sensing modality for use in landmine detection systems. FLIR-based detection systems benefit from larger standoff distances and faster rates of advance than other sensing modalities, but they also present significant challenges for detection algorithm design. FLIR video typically yields multiple looks at each object in the scene, each from a different camera perspective. As a result each object in the scene appears in multiple video frames, and each time at a different shape and size. This presents questions about how best to utilize such information. Evidence in the literature suggests such multi-look information can be exploited to improve detection performance but, to date, there has been no controlled investigation of multi-look information in detection. Any results are further confounded because no precise definition exists for what constitutes multi-look information. This thesis addresses these problems by developing a precise mathematical definition of "a look", and how to quantify the multi-look content of video data. Controlled experiments are conducted to assess the impact of multi-look information on FLIR detection using several popular detection algorithms. Based on these results two novel video processing techniques are presented, the plan-view framework and the FLRX algorithm, to better exploit multi-look information. The results show that multi-look information can have a positive or negative impact on detection performance depending on how it is used. The results also show that the novel algorithms presented here are effective techniques for analyzing video and exploiting any multi-look information to improve detection performance.

The observation and use of thermal radiation has a long history. Significant advance was made in 1879 when Josef Stefan found that “the total radiated power per unit surface area of a black body across all wavelengths is directly proportional to the fourth power of its temperature”, which was later named the Stefan–Boltzmann law. The Stefan–Boltzmann law sets a limit for the thermal radiation from most of natural materials, since their total radiated energy is proportional to the fourth power of their temperature. Thus, use of natural materials for the control and manipulation of thermal emission is hindered from further development. Metamaterials are artificial materials consisting of sub-wavelength unit cells, and good candidates to break these limitations, since the optical properties of metamaterials originates from their geometrical designs, as opposed to their chemical composition. Here we propose and demonstrate the idea of metamaterial based on microelectromechanical system capable of dynamically tailoring the energy emitted from a surface, with its emission performance going beyond the Stefan–Boltzmann law. Our dynamic metamaterial emitters have great application prospects in energy harvesting, space exploration, sensing and detecting, and many other areas. In addition, our results are not limited to the thermal infrared band, demonstrate here, but may be scaled to nearly any sub-optical range of the electromagnetic spectrum, and verify the potential of MEMS metamaterials to operate as reconfigurable multifunctional devices with unprecedented energy control capabilities.

Although metamaterial may yield advanced thermal emission control, they are difficult to apply to some applications, such as in thermal imaging and energy harvesting with thermophotovoltaics. This is because they are typically fashioned with metallic materials and thus possess low melting points, high Ohmic loss, and high thermal conductivity. Here we present an all dielectric metamaterial absorber/emitter. By overlapping the electric and magnetic dipole resonances, a high absorptive / emissive state can be achieved. Due to its great thermal properties, such as heat localization and thermal stability, an all dielectric metamaterial absorber/ emitter can replace metal-based metamaterial in some application areas, and offers a new route for applications in thermophotovoltaics, imaging, and sensing.

This dissertation consists of seven chapters. The first chapter gives a brief introduction to thermal radiation, metamaterials, metamaterial absorbers, and all dielectric metamaterials. The second chapter discusses in detail thermochromic infrared metamaterials. The third chapter demonstrates a reconfigurable room temperature metamaterial infrared emitter. The fourth chapter shows a THz all dielectric metamaterial absorber. The fifth chapter gives another example of all dielectric metamaterial emitters that can be used in thermophotovoltaic systems. The sixth chapter is a summary. The seventh chapter is an executive summary of original contributions.

The ground penetrating radar (GPR) is one of the most popular and successful sensing modalities that have been investigated for buried target detection (BTD). GPR offers excellent detection performance, however, it is limited by a low rate of advance (ROA) due to its short sensing standoff distance. Standoff distance refers to the distance between the sensing platform and the location in front of the platform where the GPR senses the ground. Large standoff (high ROA) sensing modalities have been investigated as alternatives to the GPR but they do not (yet) achieve comparable detection performance. Another strategy to improve the ROA of the GPR is to combine it with a large standoff sensor within the same BTD system, and to leverage the benefits of the respective modalities. This work investigates both of the aforementioned approaches to improve the ROA of GPR systems using statistical modeling techniques. The first part of the work investigates two large-standoff modalities for BTD systems. New detection algorithms are proposed in both cases with the goal of improving their detection performance so that it is more comparable with the GPR. The second part of the work investigates two methods of combining the GPR with a large standoff modality in order to yield a system with greater ROA, but similar target detection performance. All proposed statistical modeling approaches in this work are tested for efficacy using real field-collected data from BTD systems. The experimental results show that each of the proposed methods contribute towards the goal of improving the ROA of BTD systems.