KEYWORDS: Target detection, Sensors, Calibration, Detection and tracking algorithms, Latex, Image sensors, Staring arrays, Cameras, Signal to noise ratio, Hyperspectral imaging, Scene based nonuniformity corrections
Hyperspectral imaging sensors suffer from pixel-to-pixel response nonuniformity that manifests as fixed pattern noise (FPN) in collected data. FPN is typically removed by application of flat-field calibration procedures and nonuniformity correction algorithms. Despite application of these techniques, some amount of residual fixed pattern noise (RFPN) may persist in the data, negatively impacting target detection performance. In this paper we examine the conditions under which RFPN can impact detection performance using data collected in the SWIR across a range of target materials. We examine the application of scene-based nonuniformity correction (SBNUC) algorithms and assess their ability to remove RFPN. Moreover, we examine the effect of RFPN after application of these techniques to assess detection performance on a number of target materials that range in inherent separability from the background.
Hyperspectral images often contain hundreds of spectral bands. Man-made and natural materials usually exhibit variability in their reflective and emissive response across these bands, which is exploitable via target detection algorithms. The high-dimensional nature of hyperspectral data has driven studies that explored ways to reduce spectral dimensionality without adversely affecting spectral target detection. Recently, spatial-spectral feature extraction techniques have demonstrated additional discrimination capability versus spectral-only approaches in VNIR, SWIR, and LWIR hyperspectral imagery. When spatial descriptors are applied to spectral bands within a hyperspectral image, the length of a resulting spatial-spectral feature vector can be several times that of the original spectrum. While numerous efforts to reduce the dimensionality of hyperspectral imagery have been undertaken, they have not been commonly extended to the spatial-spectral domain. In this work, we address the relatively new problem of spatial-spectral dimensionality reduction through a strategy designed to remove features that neither negatively affect a target detection algorithm's capability to detect targets nor detract from that algorithm's ability to discriminate between targets in an exemplar signature library.
A common problem in applying lossy compression to a hyperspectral image is predicting its effect on spectral target detection performance. Recent work has shown that light amounts of lossy compression can remove noise in hyperspectral imagery that would otherwise bias a covariance-based spectral target detection algorithm’s background-normalized response to target samples. However, the detection performance of such an algorithm is a function of both the specific target of interest as well as the background, among other factors, and therefore sometimes lossy compression operating at a particular compression ratio (CR) will not negatively affect the detection of one target, while it will negatively affect the detection of another. To account for the variability in this behavior, we have developed a target-centric metric that guides the selection of a lossy compression algorithm’s CR without knowledge of whether or not the targets of interest are present in an image. Further, we show that this metric is correlated with the adaptive coherence estimator’s (ACE’s) signal to clutter ratio when targets are present in an image.
Spatial-spectral feature extraction algorithms – such as those based on spatial descriptors applied to selected spectral bands within a hyperspectral image – can provide additional discrimination capability beyond traditional spectral-only approaches. However, when attempting to detect a target with such algorithms, an exemplar target signature is often manually derived from the hyperspectral images representation in the spatial-spectral feature space. This requires a reference image in which the targets location is known. Additionally, the scenebased signature captures only the representation of the target under certain collection conditions from a specific sensor, namely, illumination level and atmospheric composition, look angle, and target pose against a specific background. A detection algorithm utilizing this spatial-spectral signature (or the spatial descriptor itself) that is sensitive to changes in these collection conditions could suffer a loss in performance should the new conditions significantly deviate from the exemplars case. To begin to overcome these limitations, we formulate and evaluate the effectiveness of a modeling technique for synthesizing exemplar spatial-spectral signatures for solid targets, particularly when the spatial structure of the target of interest varies due to pose or obscuration by the background, and when applicable, the target temperature varies. We assess the impact of these changes on a group of spatial descriptors responses to guide the modeling process for a set of two-dimensional targets specifically designed for this study. The sources of variability that most affect each descriptor are captured in target subspaces, which then form the basis of new spatial-spectral target detection algorithms.
Hyperspectral imaging (HSI) combined with target detection and identification algorithms require spectral signatures for target materials of interest. The longwave infrared (LWIR) region of the electromagnetic spectrum is dominated by thermal emission, and thus, estimates of target temperature are necessary for emissivity retrieval through temperature-emissivity separation or for conversion of known emissivity signatures to radiance units. Therefore, lack of accurate target temperature information poses a significant challenge for target detection and identification algorithms. Previous studies have demonstrated both LWIR target detection using signature subspaces and visible/shortwave subpixel target identification. This work compares adaptive coherence estimator (ACE) and subspace target detection algorithms for various target materials, atmospheric compensation algorithms, and imagery domains (radiance or emissivity) for several data sets. Preliminary results suggest that target detection in the radiance and emissivity domains is complementary, in the sense that certain material classes may be more easily detected using subspaces, while others require conversion to emissivity space. Furthermore, a radiance domain LWIR material identification algorithm that accounts for target temperature uncertainty is presented. The latter algorithm is shown to effectively distinguish between materials with a high degree of spectral similarity.
Hyperspectral imagery (HSI) offers numerous advantages over traditional sensing modalities with its high spectral content that allows for classification, anomaly detection, target discrimination, and change detection. However, this imaging modality produces a huge amount of data, which requires transmission, processing, and storage resources; hyperspectral compression is a viable solution to these challenges. It is well known that lossy compression of hyperspectral imagery can impact hyperspectral target detection. Here we examine lossy compressed hyperspectral imagery from data-centric and target-centric perspectives. The compression ratio (CR), root mean square error (RMSE), the signal to noise ratio (SNR), and the correlation coefficient are computed directly from the imagery and provide insight to how the imagery has been affected by the lossy compression process. With targets present in the imagery, we perform target detection with the spectral angle mapper (SAM) and adaptive coherence estimator (ACE) and evaluate the change in target detection performance by examining receiver operating characteristic (ROC) curves and the target signal-to-clutter ratio (SCR). Finally, we observe relationships between the data- and target-centric metrics for selected visible/near-infrared to shortwave infrared (VNIR/SWIR) HSI data, targets, and backgrounds that motivate potential prediction of change in target detection performance as a function of compression ratio.
Hyperspectral image data suffer from pixel-to-pixel response nonuniformity that degrades the imagery in the form of columnated striping noise. This nonuniformity, or fixed pattern noise (FPN), is typically compensated for through flat-field calibration procedures. FPN is a particularly challenging problem because the detector responsivities drift relative to one another in time, requiring that the sensor be periodically recalibrated. Both the rate and severity of the drift depend on a host of factors that result in varying levels of residual calibration error being present within the data at all times. Scene-based nonuniformity correction (SBNUC) algorithms estimate and remove FPN by exploiting content within the scene data and are often necessary to acceptably remove sensor artifacts for subpixel target detection applications. We present results from two SBNUC techniques that reduce residual FPN and improve target signal-to-clutter ratio. We make the observation that temporally reordering the data in conjunction with the use of spatial ratios or differentials results in algorithms that require a low number of temporal data samples to reliably correct for FPN with minimal introduction of image artifacts. Additionally, application of the algorithms within the principal components domain can further improve their correction ability.
Previous work with the Bobcat 2013 data set1 showed that spatial-spectral feature extraction on visible to near infrared (VNIR) hyperspectral imagery (HSI) led to better target detection and discrimination than spectral-only techniques; however, the aforementioned study could not consider the possible benefits of the shortwaveinfrared (SWIR) portion of the spectrum due to data limitations. In addition, the spatial resolution of the Bobcat 2013 imagery was fixed at 8cm without exploring lower spatial resolutions. In this work, we evaluate the tradeoffs in spatial and spectral resolution and spectral coverage between for a common set of targets in terms of their effects on spatial-spectral target detection performance. We show that for our spatial-spectral target detection scheme and data sets, the adaptive cosine estimator (ACE) applied to S-DAISY and pseudo Zernike moment (PZM) spatial-spectral features can distinguish between targets better than ACE applied only to the spectral imagery. In particular, S-DAISY operating on bands uniformly selected from the SWIR portion of ProSpecTIR-VS sensor imagery in conjunction with bands closely corresponding to the Airborne Real-time Cueing Hyperspectral Reconnaissance (ARCHER) sensor's VNIR bands (80 total) led to the best overall average performance in both target detection and discrimination.
The amount of hyperspectral imagery (HSI) data currently available is relatively small compared to other imaging modalities, and what is suitable for developing, testing, and evaluating spatial-spectral algorithms is virtually nonexistent. In this work, a significant amount of coincident airborne hyperspectral and high spatial resolution panchromatic imagery that supports the advancement of spatial-spectral feature extraction algorithms was collected to address this need. The imagery was collected in April 2013 for Ohio University by the Civil Air Patrol, with their Airborne Real-time Cueing Hyperspectral Enhanced Reconnaissance (ARCHER) sensor. The target materials, shapes, and movements throughout the collection area were chosen such that evaluation of change detection algorithms, atmospheric compensation techniques, image fusion methods, and material detection and identification algorithms is possible. This paper describes the collection plan, data acquisition, and initial analysis of the collected imagery.
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.
The nature of hyperspectral exploitation systems is such that a set of spectral imagery - and possibly a priori information
such as a supplied library of target spectral signatures - is ingested into an algorithm and a series of responses is output.
These responses must be scored for their accuracy against known target locations in the image set, from which algorithm
performance is then determined. We propose, implement, and demonstrate a new environment for visualizing this
process, which will aid not only the evaluator but also the algorithm developer in better understanding, characterizing,
and improving system performance, be it that of an anomaly detection, change detection, or material identification
algorithm.
Hyperspectral imagery (HSI) is a relatively new technology capable of relaying intensity information gathered from both
visible and non-visible ranges of the electromagnetic spectrum. HSI images can contain hundreds of bands, which
present a problem when an image analyst must select the most relevant bands from such an image for visualization,
particularly when the bands that are within the range of human vision are either not present or heavily distorted. It is
proposed here that two-dimensional principal component analysis (2DPCA) can aid in the automatic selection of the
bands from an HSI image that would best reflect visual information. The method requires neither prior knowledge of the
image contents nor the association between spectral bands and their center wavelengths.
Biometric identification relies on information that is difficult to misplace or duplicate, making it a very useful tool when properly implemented. One biometric feature of considerable interest is the iris. Since most people rely heavily on their vision, they are protective of their eyes. This means there is less likelihood of change due to environmental factors. In addition, since the iris is created in a random morphogenetic process, there is a large amount of complexity suitable for use as a discriminator.
There are currently several powerful methods available for using the human iris as a biometric for identification. One drawback inherent in the existing methods, however, is their computational complexity. Adopting stochastic models can provide an approach to reducing the extensive computing burden. To this end, we have presented two methods that rely on a wide-sense stationary approximation to the texture and gray scale information in the iris; one uses auto- and cross-correlations while the other employs second order statistics of co-occurrence matrices. Our experiments indicate that cross- and auto-correlations and co-occurrence matrix features are likely to be prominent iris discriminators for correct identification.
Future tests will be conducted on larger sample sets to further verify the findings presented here. Two main methods for feature generation will also be compared and combined to produce an optimal classification strategy for an embedded hardware realization of the method. The addition of more features for discrimination is a likely necessity for classifying larger numbers of irises.
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