Hyperspectral Imaging for Landmine Detection

This PhD thesis aims at investigating the possibility to detect landmines using hyperspectral imaging. Using this technology, we are able to acquire at each pixel of the image spectral data in hundreds of wavelengths. So, at each pixel we obtain a reflectance spectrum that is used as fingerprint to identify the materials in each pixel, and mainly in our project help us to detect the presence of landmines. The proposed process works as follows: a preconfigured drone (hexarotor or octorotor) will carry the hyperspectral camera. This programmed drone is responsible of flying over the contaminated area in order to take images from a safe distance. Various image processing techniques will be used to treat the image in order to isolate the landmine from the surrounding. Once the presence of a mine or explosives is suspected, an alarm signal is sent to the base station giving information about the type of the mine, its location and the clear path that could be taken by the mine removal team in order to disarm the mine. This technology has advantages over the actually used techniques: • It is safer because it limits the need of humans in the searching process and gives the opportunity to the demining team to detect the mines while they are in a safe region. • It is faster. A larger area could be cleared in a single day by comparison with demining techniques • This technique can be used to detect at the same time objects other than mines such oil or minerals. First, a presentation of the problem of landmines that is expanding worldwide referring to some statistics from the UN organizations is provided. In addition, a brief presentation of different types of landmines is shown. Unfortunately, new landmines are well camouflaged and are mainly made of plastic in order to make their detection using metal detectors harder. A summary of all landmine detection techniques is shown to give an idea about the advantages and disadvantages of each technique. In this work, we give an overview of different projects that worked on the detection of landmines using hyperspectral imaging. We will show the main results achieved in this field and future work to be done in order to make this technology effective. Moreover, we worked on different target detection algorithms in order to achieve high probability of detection with low false alarm rate. We tested different statistical and linear unmixing based methods. In addition, we introduced the use of radial basis function neural networks in order to detect landmines at subpixel level. A comparative study between different detection methods will be shown in the thesis. A study of the effect of dimensionality reduction using principal component analysis prior to classification is also provided. The study shows the dependency between the two steps (feature extraction and target detection). The selection of target detection algorithm will define if feature extraction in previous phase is necessary. A field experiment has been done in order to study how the spectral signature of landmine will change depending on the environment in which the mine is planted. For this, we acquired the spectral signature of 6 types of landmines in different conditions: in Lab where specific source of light is used; in field where mines are covered by grass; and when mines are buried in soil. The results of this experiment are very interesting. The signature of two types of landmines are used in the simulations. They are a database necessary for supervised detection of landmines. Also we extracted some spectral characteristics of landmines that would help us to distinguish mines from background

Détection robuste de cibles en imagerie Hyperspectrale.

Hyperspectral imaging (HSI) extends from the fact that for any given material, the amount of emitted radiation varies with wavelength. HSI sensors measure the radiance of the materials within each pixel area at a very large number of contiguous spectral bands and provide image data containing both spatial and spectral information. Classical adaptive detection schemes assume that the background is zero-mean Gaussian or with known mean vector that can be exploited. However, when the mean vector is unknown, as it is the case for hyperspectral imaging, it has to be included in the detection process. We propose in this work an extension of classical detection methods when both covariance matrix and mean vector are unknown.However, the actual multivariate distribution of the background pixels may differ from the generally used Gaussian hypothesis. The class of elliptical distributions has already been popularized for background characterization in HSI. Although these non-Gaussian models have been exploited for background modeling and detection schemes, the parameters estimation (covariance matrix, mean vector) is usually performed using classical Gaussian-based estimators. We analyze here some robust estimation procedures (M-estimators of location and scale) more suitable when non-Gaussian distributions are assumed. Jointly used with M-estimators, these new detectors allow to enhance the target detection performance in non-Gaussian environment while keeping the same performance than the classical detectors in Gaussian environment. Therefore, they provide a unified framework for target detection and anomaly detection in HSI.L'imagerie hyperspectrale (HSI) repose sur le fait que, pour un matériau donné, la quantité de rayonnement émis varie avec la longueur d'onde. Les capteurs HSI mesurent donc le rayonnement des matériaux au sein de chaque pixel pour un très grand nombre de bandes spectrales contiguës et fournissent des images contenant des informations à la fois spatiale et spectrale. Les méthodes classiques de détection adaptative supposent généralement que le fond est gaussien à vecteur moyenne nul ou connu. Cependant, quand le vecteur moyen est inconnu, comme c'est le cas pour l'image hyperspectrale, il doit être inclus dans le processus de détection. Nous proposons dans ce travail d'étendre les méthodes classiques de détection pour lesquelles la matrice de covariance et le vecteur de moyenne sont tous deux inconnus.Cependant, la distribution statistique multivariée des pixels de l'environnement peut s'éloigner de l'hypothèse gaussienne classiquement utilisée. La classe des distributions elliptiques a été déjà popularisée pour la caractérisation de fond pour l’HSI. Bien que ces modèles non gaussiens aient déjà été exploités dans la modélisation du fond et dans la conception de détecteurs, l'estimation des paramètres (matrice de covariance, vecteur moyenne) est encore généralement effectuée en utilisant des estimateurs conventionnels gaussiens. Dans ce contexte, nous analysons de méthodes d’estimation robuste plus appropriées à ces distributions non-gaussiennes : les M-estimateurs. Ces méthodes de détection couplées à ces nouveaux estimateurs permettent d'une part, d'améliorer les performances de détection dans un environment non-gaussien mais d'autre part de garder les mêmes performances que celles des détecteurs conventionnels dans un environnement gaussien. Elles fournissent ainsi un cadre unifié pour la détection de cibles et la détection d'anomalies pour la HSI

Application of Multi-Sensor Fusion Technology in Target Detection and Recognition

Application of multi-sensor fusion technology has drawn a lot of industrial and academic interest in recent years. The multi-sensor fusion methods are widely used in many applications, such as autonomous systems, remote sensing, video surveillance, and the military. These methods can obtain the complementary properties of targets by considering multiple sensors. On the other hand, they can achieve a detailed environment description and accurate detection of interest targets based on the information from different sensors.This book collects novel developments in the field of multi-sensor, multi-source, and multi-process information fusion. Articles are expected to emphasize one or more of the three facets: architectures, algorithms, and applications. Published papers dealing with fundamental theoretical analyses, as well as those demonstrating their application to real-world problems

A Physical Model of Human Skin and Its Application for Search and Rescue

For this research we created a human skin reflectance model in the VIS and NIR. We then modeled sensor output for an RGB sensor based on output from the skin reflectance model. The model was also used to create a skin detection algorithm and a skin pigmentation level (skin reflectance at 685nm) estimation algorithm. The average root mean square error across the VIS and NIR between the skin reflectance model and measured data was 2%. The skin reflectance model then allowed us to generate qualitatively accurate responses for an RGB sensor for different biological and lighting conditions. To test the accuracy of the skin detection and skin color estimation algorithms, hyperspectral images of a suburban test scene containing people with various skin colors were collected. The skin detection algorithm had a probability of detection as high as 95% with a probability of false alarm of 0.6%. The skin pigmentation level estimation algorithm had a mean absolute error when compared with data measured by a reflectometer of 2.6% where the reflectance of the individuals at 685nm ranged from 14% to 64%

Polarimetric SAR Change Detection with the Complex Hotelling-Lawley Trace Statistic

Accepted manuscript version. Published version at http://dx.doi.org/10.1109/TGRS.2016.2532320.In this paper, we propose a new test statistic for unsupervised change detection in polarimetric radar images. We work with multilook complex covariance matrix data, whose underlying model is assumed to be the scaled complex Wishart distribution. We use the complex-kind Hotelling-Lawley trace statistic for measuring the similarity of two covariance matrices. The distribution of the Hotelling-Lawley trace statistic is ap- proximated by a Fisher-Snedecor distribution, which is used to define the significance level of a false alarm rate regulated change detector. Experiments on simulated and real PolSAR data sets demonstrate that the proposed change detection method gives detections rates and error rates that are comparable with the generalized likelihood ratio test

An Examination of Environmental Applications for Uncooled Thermal Infrared Remote Sensing Instruments

Advancements in system design for thermal instruments require assessment of potential environmental applications and appropriate data processing techniques. A novel multi-band thermal imaging system was proposed by DRS Leonardo for the National Aeronautics and Space Administration Earth Science Technology Office Instrument Incubator Program, for which these criteria were assessed. The Multi-Band Uncooled Radiometer Imager (MURI) is a six spectral channel instrument designed to collect images in the thermal infrared, specifically in the range of 7.5 to 12.5 μm. The work detailed in this thesis characterizes the ability of a thermal imager with an uncooled microbolometer focal plane array to provide valuable data for environmental science applications. Here, a pair of studies using simulated data demonstrates the ability of a multispectral instrument such as MURI to detect enhanced levels of atmospheric methane using a novel approach that performs similarly to a state of the art algorithm when applied to MURI data. The novel method is evaluated using a controlled concentration simulated dataset to determine the extent of its detection capabilities and its dependence on atmospheric conditions. The methane investigations reveal the system is capable of detecting a 20 m thick CH4 plume of 10-20 ppm above background levels when column water vapor is low using both the NDMI and matched filter approaches. Additionally, land surface temperature and emissivity retrieval techniques were applied to experimental MURI data recorded during initial test flights to assess their accuracy with MURI data. Utilizing split window and Temperature Emissivity Separation make this examination distinct as this establishes that proven methods can be applied to uncooled multiband imager data. Application of these methods to MURI data demonstrated the system is capable of temperature retrievals with Root Mean Square Errors of less than 1 K to measured reference values and surface emissivity retrievals within 2% of reference database values. The definition and application of the Normalized Differential Methane Index in this thesis demonstrates a novel approach for detection of enhanced plumes of methane utilizing a multispectral system with only a single band allocated to methane absorption features

The state of the art in monitoring and verification— ten years on

In the ten years since publication of the IPCC Special Report on CCS, there has been considerable progress in monitoring and verification (M&V). Numerous injection projects, ranging from small injection pilots to much larger longer-term commercial operations, have been successfully monitored to the satisfaction of regulatory agencies, and technologies have been adapted and implemented to demonstrate containment, conformance, and no environmental impact. In this review we consider M&V chiefly from the perspective of its ability to satisfy stakeholders that these three key requirements are being met. From selected project examples, we show how this was done, and reflect particularly on the nature of the verification process. It is clear that deep-focussed monitoring will deliver the primary requirement to demonstrate conformance and containment and to provide early warning of any deviations from predicted storage behaviour. Progress in seismic imaging, especially offshore, and the remarkable results with InSAR from In Salah are highlights of the past decade. A wide range of shallow monitoring techniques has been tested at many sites, focussing especially on the monitoring of soil gas and groundwater. Quantification of any detected emissions would be required in some jurisdictions to satisfy carbon mitigation targets in the event of leakage to surface: however, given the likely high security of foreseeable storage sites, we suggest that shallow monitoring should focus mainly on assuring against environmental impacts. This reflects the low risk profile of well selected and well operated storage sites and recognizes the over-arching need for monitoring to be directed to specific, measureable risks. In particular, regulatory compliance might usefully involve clearer articulation of leakage scenarios, with this specificity making it possible to demonstrate “no leakage” in a more objective way than is currently the case. We also consider the monitoring issues for CO2-EOR, and argue that there are few technical problems in providing assurance that EOR sites are successfully sequestering CO2; the issues lie largely in linking existing oil and gas regulations to new greenhouse gas policy. We foresee that, overall, monitoring technologies will continue to benefit from synergies with oil and gas operations, but that the distinctive regulatory and certification environments for CCS may pose new questions. Overall, while there is clearly scope for technical improvements, more clearly posed requirements, and better communication of monitoring results, we reiterate that this has been a decade of significant achievement that leaves monitoring and verification well placed to serve the wider CCS enterprise

Spectral LADAR: Active Range-Resolved Imaging Spectroscopy

Imaging spectroscopy using ambient or thermally generated optical sources is a well developed technique for capturing two dimensional images with high per-pixel spectral resolution. The per-pixel spectral data is often a sufficient sampling of a material's backscatter spectrum to infer chemical properties of the constituent material to aid in substance identification. Separately, conventional LADAR sensors use quasi-monochromatic laser radiation to create three dimensional images of objects at high angular resolution, compared to RADAR. Advances in dispersion engineered photonic crystal fibers in recent years have made high spectral radiance optical supercontinuum sources practical, enabling this study of Spectral LADAR, a continuous polychromatic spectrum augmentation of conventional LADAR. This imaging concept, which combines multi-spectral and 3D sensing at a physical level, is demonstrated with 25 independent and parallel LADAR channels and generates point cloud images with three spatial dimensions and one spectral dimension. The independence of spectral bands is a key characteristic of Spectral LADAR. Each spectral band maintains a separate time waveform record, from which target parameters are estimated. Accordingly, the spectrum computed for each backscatter reflection is independently and unambiguously range unmixed from multiple target reflections that may arise from transmission of a single panchromatic pulse. This dissertation presents the theoretical background of Spectral LADAR, a shortwave infrared laboratory demonstrator system constructed as a proof-of-concept prototype, and the experimental results obtained by the prototype when imaging scenes at stand off ranges of 45 meters. The resultant point cloud voxels are spectrally classified into a number of material categories which enhances object and feature recognition. Experimental results demonstrate the physical level combination of active backscatter spectroscopy and range resolved sensing to produce images with a level of complexity, detail, and accuracy that is not obtainable with data-level registration and fusion of conventional imaging spectroscopy and LADAR. The capabilities of Spectral LADAR are expected to be useful in a range of applications, such as biomedical imaging and agriculture, but particularly when applied as a sensor in unmanned ground vehicle navigation. Applications to autonomous mobile robotics are the principal motivators of this study, and are specifically addressed

Geophysical methods to detect tunnelling at a geological repository site : Applicability in safeguards

ABSTRACT Generating power with nuclear energy accumulates radioactive spent nuclear fuel, anticipated not to be diversified into any unknown purposes. Nuclear safeguards include bookkeeping of nuclear fuel inventories, frequent checking, and monitoring to confirm nuclear non-proliferation. Permanent isolation of radionuclides from biosphere by disposal challenges established practices, as opportunities for monitoring of individual fuel assemblies ceases. Different concepts for treatment and geological disposal of spent nuclear fuel exist. Spent nuclear fuel disposal facility is under construction in Olkiluoto in Southwest Finland. Posiva Oy has carried out multidisciplinary bedrock characterization of crystalline bedrock for siting and design of the facility. Site description involved compilation of geological models from investigations at surface level, from drillholes and from underground rock characterization facility ONKALO. Research focused on long term safety case (performance) of engineered and natural barriers in purpose to minimize risks of radionuclide release. Nuclear safeguards include several concepts. Containment and surveillance (C/S) are tracking presence of nuclear fuel through manufacturing, energy generation, cooling, transfer, and encapsulation. Continuity of knowledge (CoK) ensures traceability and non-diversion. Design information provided by the operator to the state and European Commission (Euratom), and further to IAEA describes spent nuclear fuel handling in the facility. Design information verification (DIV) using timely or unannounced inspections, provide credible assurance on absence of any ongoing undeclared activities within the disposal facility. Safeguards by design provide information applicable for the planning of safeguards measures, e.g., surveillance during operation of disposal facility. Probability of detection of an attempt to any undeclared intrusion into the repository containment needs to be high. Detection of such preparations after site closure would require long term monitoring or repeated geophysical measurements within or at proximity of the repository. Bedrock imaging (remote sensing, geophysical surveys) would serve for verifying declarations where applicable, or for characterization of surrounding rock mass to detect undeclared activities. ASTOR working group has considered ground penetrating radar (GPR) for DIV in underground constructed premises during operation. Seismic reflection survey and electrical or electromagnetic imaging may also apply. This report summarizes geophysical methods used in Olkiluoto, and some recent development, from which findings could be applied also for nuclear safeguards. In this report the geophysical source fields, involved physical properties, range of detection, resolution, survey geometries, and timing of measurements are reviewed for different survey methods. Useful interpretation of geophysical data may rely on comparison of results to declared repository layout, since independent understanding of the results may not be successful. Monitoring provided by an operator may enable alarm and localization of an undeclared activity in a cost-effective manner until closure of the site. Direct detection of constructed spaces, though possible, might require repeated effort, have difficulties to provide spatial coverage, and involve false positive alarms still requiring further inspection