Using Lidar to geometrically-constrain signature spaces for physics-based target detection

A fundamental task when performing target detection on spectral imagery is ensuring that a target signature is in the same metric domain as the measured spectral data set. Remotely sensed data are typically collected in digital counts and calibrated to radiance. That is, calibrated data have units of spectral radiance, while target signatures in the visible regime are commonly characterized in units of re°ectance. A necessary precursor to running a target detection algorithm is converting the measured scene data and target signature to the same domain. Atmospheric inversion or compensation is a well-known method for transforming mea- sured scene radiance values into the re°ectance domain. While this method may be math- ematically trivial, it is computationally attractive and is most e®ective when illumination conditions are constant across a scene. However, when illumination conditions are not con- stant for a given scene, signi¯cant error may be introduced when applying the same linear inversion globally. In contrast to the inversion methodology, physics-based forward modeling approaches aim to predict the possible ways that a target might appear in a scene using atmospheric and radiometric models. To fully encompass possible target variability due to changing illumination levels, a target vector space is created. In addition to accounting for varying illumination, physics-based model approaches have a distinct advantage in that they can also incorporate target variability due to a variety of other sources, to include adjacency target orientation, and mixed pixels. Increasing the variability of the target vector space may be beneficial in a global sense in that it may allow for the detection of difficult targets, such as shadowed or partially concealed targets. However, it should also be noted that expansion of the target space may introduce unnecessary confusion for a given pixel. Furthermore, traditional physics-based approaches make certain assumptions which may be prudent only when passive, spectral data for a scene are available. Common examples include the assumption of a °at ground plane and pure target pixels. Many of these assumptions may be attributed to the lack of three-dimensional (3D) spatial information for the scene. In the event that 3D spatial information were available, certain assumptions could be levied, allowing accurate geometric information to be fed to the physics-based model on a pixel- by-pixel basis. Doing so may e®ectively constrain the physics-based model, resulting in a pixel-specific target space with optimized variability and minimized confusion. This body of work explores using spatial information from a topographic Light Detection and Ranging (Lidar) system as a means to enhance the delity of physics-based models for spectral target detection. The incorporation of subpixel spatial information, relative to a hyperspectral image (HSI) pixel, provides valuable insight about plausible geometric con¯gurations of a target, background, and illumination sources within a scene. Methods for estimating local geometry on a per-pixel basis are introduced; this spatial information is then fed into a physics-based model to the forward prediction of a target in radiance space. The target detection performance based on this spatially-enhanced, spectral target space is assessed relative to current state-of-the-art spectral algorithms

Signal and data processing for machine olfaction and chemical sensing: A review

Signal and data processing are essential elements in electronic noses as well as in most chemical sensing instruments. The multivariate responses obtained by chemical sensor arrays require signal and data processing to carry out the fundamental tasks of odor identification (classification), concentration estimation (regression), and grouping of similar odors (clustering). In the last decade, important advances have shown that proper processing can improve the robustness of the instruments against diverse perturbations, namely, environmental variables, background changes, drift, etc. This article reviews the advances made in recent years in signal and data processing for machine olfaction and chemical sensing

An inside vs. outside classification system for Wi-Fi IoT devices

We are entering an era in which Smart Devices are increasingly integrated into our daily lives. Everyday objects are gaining computational power to interact with their environments and communicate with each other and the world via the Internet. While the integration of such devices offers many potential benefits to their users, it also gives rise to a unique set of challenges. One of those challenges is to detect whether a device belongs to one’s own ecosystem, or to a neighbor – or represents an unexpected adversary. An important part of determining whether a device is friend or adversary is to detect whether a device’s location is within the physical boundaries of one’s space (e.g. office, classroom, home). In this thesis we propose a system that is able to decide with 82% accuracy whether the location of an IoT device is inside or outside of a defined space based on a small number of transmitted Wi- Fi frames. The classification is achieved by leveraging a machine-learning classifier trained and tested on RSSI data of Wi-Fi transmissions recorded by three or more observers. In an initialization phase the classifier is trained by the user on Wi-Fi transmissions of a variety of locations, inside (and outside). The system can be built with off-the-shelf Wi-Fi observing devices that do not require any special hardware modifications. With the exception of the training period, the system can accurately classify the indoor/outdoor state of target devices without any cooperation from the user or from the target devices

Service robotics and machine learning for close-range remote sensing

L'abstract è presente nell'allegato / the abstract is in the attachmen

A Characterization of Human Burial Signatures using Spectroscopy and LIDAR

This study is an analysis of terrestrial remote sensing data sets collected at the University of Tennessee’s Anthropology Research Facility (ARF). The objective is to characterize human burial signatures using spectroscopy and laser scanning technologies. The development of remote human burial detection methodologies depends on basic research to establish signatures that inform forensic investigations. This dissertation provides recommendations for future research on remote sensing of human burials, and for investigators who wish to apply these technologies to case work. Data used in this study include terrestrial spectra, aerial hyperspectral imagery, satellite multispectral imagery, terrestrial light detection and ranging (LIDAR), and aerial LIDAR. In February 2013, ten individuals donated through the Forensic Anthropology Center body donation program were buried in three differently sized graves at the ARF. The graves contain one, three, and six bodies, respectively. An empty experimental control grave was also created. Terrestrial data collections were made from two-days pre-burial to 21-months post-burial. Aerial data were collected from 19 to 27-months post-burial. Satellite imagery was collected from six-months pre-burial to 23-months post-burial. Analytical emphasis is placed on the terrestrial data sets, which are of the highest spatial and spectral fidelity. Results of terrestrial data analysis reveal separable spectral and topographic signatures between the disturbed locations and surrounding undisturbed area. Aerial and satellite data were used to attempt validation of terrestrial data analysis findings, but findings were inconclusive. This study demonstrates that live vegetation spectral samples can be correctly classified as disturbed or undisturbed groups at rates from 52.0 – 78.3% using statistically-based classification models. Additionally, this study documents localized elevation change at burial surfaces as a result of initial digging activity, subsequent soil settling and subsurface decomposition. The findings of this research are significant to both researchers and practitioners. It is the first study to compare live vegetation spectra associated with human burials and is the first to document burial elevation change using LIDAR. This work contributes to a collective understanding of human burial signatures that can be used together or with other geophysical methods to assist in locating unmarked human burials