David W. Messinger

Anomaly detection using topology

In this paper we present a new topology-based algorithm for anomaly detection in dimensionally large datasets. The motivating application is hyperspectral imaging where the dataset can be a collection of ~ 106 points in Rk, representing the reflected (or radiometric) spectra of electromagnetic radiation. The algorithm begins by building a graph whose edges connect close pairs of points. The background points are the points in the largest components of this graph and all other points are designated as anomalies. The anomalies are ranked according to their distance to the background. The algorithm is termed Topological Anomaly Detection (TAD). The algorithm is tested on hyperspectral imagery collected with the HYDICE sensor which contains targets of known reflectance and spatial location. Anomaly maps are created and compared to results from the common anomaly detection algorithm RX. We show that the TAD algorithm performs better than RX by achieving greater separation of the anomalies from the background for this dataset.

Spatially Adaptive Hyperspectral Unmixing

Spectral unmixing is a common task in hyperspectral data analysis. In order to sufficiently spectrally unmix the data, three key steps must be accomplished: Estimate the number of endmembers (EMs), identify the EMs, and then unmix the data. Several different statistical and geometrical approaches have been developed for all steps of the unmixing process. However, many of these methods rely on using the full image to estimate the number and extract the EMs from the background data. In this paper, spectral unmixing is accomplished using a spatially adaptive approach. Linear unmixing is performed per pixel with EMs identified at the local level, but global abundance maps are created by clustering the locally determined EMs into common groups. Results show that the unmixing residual error of each pixels spectrum from real data, estimated from the spatially adaptive methodology, is reduced when compared to a global scale EM estimation and linear unmixing methodology. The component algorithms of the new spatially adaptive approach, which complete the three key unmixing steps, can be interchanged while maintaining spatial information, making this new methodology modular. A final advantage of the spatially adaptive spectral unmixing methodology is the user-defined spatial scale size.

Nearest-neighbor diffusion-based pan-sharpening algorithm for spectral images

Abstract. Commercial multispectral satellite datasets, such as WorldView-2 and Geoeye-1 images, are often delivered with a high-spatial resolution panchromatic image (PAN) as well as a corresponding lower resolution multispectral image (MSI). Certain fine features are only visible on the PAN but are difficult to discern on the MSI. To fully utilize the high-spatial resolution of the PAN and the rich spectral information from the MSI, a pan-sharpening process can be carried out. However, difficulties arise in maintaining radiometric accuracy, particularly for applications other than visual assessment. We propose a fast pan-sharpening process based on nearest-neighbor diffusion with the aim to enhance the salient spatial features while preserving spectral fidelity. Our approach assumes that each pixel spectrum in the pan-sharpened image is a weighted linear mixture of the spectra of its immediate neighboring superpixels; it treats each spectrum as its smallest element of operation, which is different from the most existing algorithms that process each band separately. Our approach is shown to be capable of preserving salient spatial and spectral features. We expect this algorithm to facilitate fine feature extraction from satellite images.

Geospatial Disaster Response during the Haiti Earthquake: A Case Study Spanning Airborne Deployment, Data Collection, Transfer, Processing, and Dissemination

Immediately following the 12 January 2010 earthquake in Haiti, a disaster response team from Rochester Institute of Technology, ImageCat Inc., and Kucera International, funded by the Global Facility for Disaster Reduction and Recovery group of the World Bank, collected 0.15 m airborne imagery and two points/m2 lidar data for 650 km2 over a period of seven days. Data were transferred to Rochester, New York for processing at rates that approached 400 Mb/s using Internet2, ortho-rectified with a 24-hour turnaround, and distributed to response agencies through file or disk transfer. A unique response effort, dubbed the Global Earth Observation - Catastrophe Assessment Network (GEO-CAN) and headed by ImageCat, utilized over 600 experts from 23 different countries to generate rapid turnaround damage assessment products. This paper highlights the airborne data collection, transfer, processing, and product development effort, which arguably has raised the bar in terms of response to large-scale disasters.

The SHARE 2012 data campaign

A multi-modal (hyperspectral, multispectral, and LIDAR) imaging data collection campaign was conducted just south of Rochester New York in Avon, NY on September 20, 2012 by the Rochester Institute of Technology (RIT) in conjunction with SpecTIR, LLC, the Air Force Research Lab (AFRL), the Naval Research Lab (NRL), United Technologies Aerospace Systems (UTAS) and MITRE. The campaign was a follow on from the SpecTIR Hyperspectral Airborne Rochester Experiment (SHARE) from 2010. Data was collected in support of the eleven simultaneous experiments described here. The airborne imagery was collected over four different sites with hyperspectral, multispectral, and LIDAR sensors. The sites for data collection included Avon, NY, Conesus Lake, Hemlock Lake and forest, and a nearby quarry. Experiments included topics such as target unmixing, subpixel detection, material identification, impacts of illumination on materials, forest health, and in-water target detection. An extensive ground truthing effort was conducted in addition to collection of the airborne imagery. The ultimate goal of the data collection campaign is to provide the remote sensing community with a shareable resource to support future research. This paper details the experiments conducted and the data that was collected during this campaign.

Enhanced detection and visualization of anomalies in spectral imagery

Anomaly detection algorithms applied to hyperspectral imagery are able to reliably identify man-made objects from a natural environment based on statistical/geometric likelyhood. The process is more robust than target identification, which requires precise prior knowledge of the object of interest, but has an inherently higher false alarm rate. Standard anomaly detection algorithms measure deviation of pixel spectra from a parametric model (either statistical or linear mixing) estimating the image background. The topological anomaly detector (TAD) creates a fully non-parametric, graph theory-based, topological model of the image background and measures deviation from this background using codensity. In this paper we present a large-scale comparative test of TAD against 80+ targets in four full HYDICE images using the entire canonical target set for generation of ROC curves. TAD will be compared against several statistics-based detectors including local RX and subspace RX. Even a perfect anomaly detection algorithm would have a high practical false alarm rate in most scenes simply because the user/analyst is not interested in every anomalous object. To assist the analyst in identifying and sorting objects of interest, we investigate coloring of the anomalies with principle components projections using statistics computed from the anomalies. This gives a very useful colorization of anomalies in which objects of similar material tend to have the same color, enabling an analyst to quickly sort and identify anomalies of highest interest.

Interstellar polarization in the taurus dark clouds, wavelength dependent position angles and cloud structure near tmc-1

Systematic variations with wavelength in the position angle of interstellar linear polarization of starlight may be indicative of multiple cloud structure along the line of sight. We use polarimetric observations of two stars (HD 29647 and HD 283809) in the general direction of TMC-1 in the Taurus dark cloud to investigate grain properties and cloud structure in this region. We show the data to be consistent with a simple two-component model in which general interstellar polarization in the Taurus cloud is produced by a widely distributed cloud component with relatively uniform magnetic field orientation; light from stars close to TMC-1 suffers additional polarization arising in one (or more) subcloud(s) with larger average grain size and magnetic field directions different from the general trend. Toward HD 29647 in particular, we show that the unusually low degree of visual polarization relative to extinction is due to depolarization associated with the presence of distinct cloud components in the line of sight with markedly different magnetic field orientations. Stokes parameter calculations allow us to separate the polarization characteristics of the individual components. Results are fitted with the Serkowski empirical formula to determine the degree and wavelength of maximum polarization. Whereas λmax values in the widely distributed material are similar to the average (0.55 μm) for the diffuse interstellar medium, the subcloud in the line of sight to HD 283809, the most heavily reddened star in our study, has λmax ≈ 0.73 μm, indicating the presence of grains ~30% larger than this average. Our model also predicts detectable levels of circular polarization toward both HD 29647 and HD 283809.

Iterative convex hull volume estimation in hyperspectral imagery for change detection

Historically in change detection, statistically based methods have been used. However, as the spatial resolution of spectral images improves, the data no longer maintain a Gaussian distribution, and some assumptions about the data - and subsequently all algorithms based upon those statistical assumptions - fail. Here we present the Simplex Volume Estimation algorithm (SVE), which avoids these potential hindrances by taking a geometrical approach. In particular, we employ the linear mixture model to approximate the convex hull enclosing the data through identification of the simplex vertices (known as endmembers). SVE begins by processing an image and tiling it into squares. Next, SVE iterates through the tiles and for each set of pixels it identifies the number of corners (as vectors) that define the simplex of that set of data. For each tile, it then iterates through the increasing dimensionality, or number of endmembers, while every time calculating the volume of the simplex that is defined by that number of endmembers. When the volume is calculated in a dimension that is higher than that of the inherent dimensionality of the data, the volume will theoretically drop to zero. This value is indicative of the inherent dimensionality of the data as represented by the convex hull. Further, the volume of the simplex will fluctuate when a new material is introduced to the dataset, indicating a change in the image. The algorithm then analyzes the volume function associated with each tile and assigns the tile a metric value based on that function. The values of these metrics will be compared by using hyperspectral imagery collected from different platforms over experimental setups with known changes between flights. Results from these tests will be presented along with a path forward for future research.

DETECTION OF GASEOUS EFFLUENTS FROM AIRBORNE LWIR HYPERSPECTRAL IMAGERY USING PHYSICS-BASED SIGNATURES

Detection of gaseous effluent plumes from airborne platforms provides a unique challenge to the remote sensing community. The measured signatures are a complicated combination of phenomenology including effects of the atmosphere, spectral characteristics of the background material under the plume, temperature contrast between the gas and the surface, and the concentration of the gas. All of these quantities vary spatially further complicating the detection problem. In complex scenes simple estimation of a “residual” spectrum may not be possible due to the variability in the scene background. A common detection scheme uses a matched filter formalism to compare laboratory-measured gas absorption spectra with measured pixel radiances. This methodology can not account for the variable signature strengths due to concentration path length and temperature contrast, nor does it take into account measured signatures that are observed in both absorption and emission in the same scene. We have developed a physics-based, forward model to predict in-scene signatures covering a wide range in gas / surface properties. This target space is reduced to a set of basis vectors using a geometrical model of the space. Corresponding background basis vectors are derived to describe the non-plume pixels in the image. A Generalized Likelihood Ratio Test is then used to discriminate between plume and non-plume pixels. Several species can be tested for iteratively. The algorithm is applied to airborne LWIR hyperspectral imagery collected by the Airborne Hyperspectral Imager (AHI) over a chemical facility with some ground truth. When compared to results from a clutter matched filter the physics-based signature approach shows significantly improved performance for the data set considered here.

SpecTIR hyperspectral airborne Rochester experiment data collection campaign

A multi-modal (hyperspectral, LiDAR, and multi-spectral) imaging data collection campaign was conducted at the Rochester Institute of Technology (RIT) in conjunction with SpecTIR, LLC, in the Rochester, New York, area July 26-29, 2010. The campaign was titled the SpecTIR Hyperspectral Airborne Rochester Experiment (SHARE) and collected data in support of nine simultaneous unique experiments, several of which leveraged data from multiple modalities. Airborne imagery was collected over the city of Rochester with hyperspectral, multispectral, and Light Detection and Ranging (LiDAR) sensors. Sites for data collection included the Genesee River, sections of downtown Rochester, and the RIT campus. Experiments included sub-pixel target detection, water quality monitoring, thermal vehicle tracking and wetlands health assessment. An extensive ground truthing effort was accomplished in addition to the airborne imagery collected. The ultimate goal of this comprehensive data collection campaign was to provide a community sharable resource that would support additional experiments. This paper details the experiments conducted and the corresponding data that were collected in conjunction with this campaign.