Perceptual Display Strategies of Hyperspectral Imagery Based on PCA and ICA

This study investigated appropriate methodologies for displaying hyperspectral imagery based on knowledge of human color vision as applied to Hyperion and AVIRIS data. Principal Component Analysis (PCA) and Independent Component Analysis (ICA) were used to reduce the data dimensionality in order to make the data more amenable to visualization in three-dimensional color space. In addition, these two methods were chosen because of their underlying relationships to the opponent color model of human color perception. PCA and ICA-based visualization strategies were then explored by mapping the first three PCs or ICs to several opponent color spaces including CIELAB, HSV, YCrCb, and YUV. The gray world assumption, which states that given an image with sufficient amount of color variations, the average color should be gray, was used to set the mapping origins. The rendered images are well color balanced and can offer a first look capability or initial classification for a wide variety of spectral scenes

Gas Plume Species Identification in Airborne LWIR Imagery Using Constrained Stepwise Regression Analyses

Identification of constituent gases in effluent plumes is performed using linear least-squares regression techniques. Airborne thermal hyperspectral imagery is used for this study. Synthetic imagery is employed as the test-case for algorithm development. Synthetic images are generated by the Digital Imaging and Remote Sensing Image Generation (DIRSIG) Model. The use of synthetic data provides a direct measure of the success of the algorithm through the comparison with truth map outputs. In image test-cases, plumes emanating from factory stacks will have been identified using a separate detection algorithm. The gas identification algorithm being developed in this work is performed only on pixels having been determined to contain the plume. Constrained stepwise linear regression is used in this study. Results indicate that the ability of the algorithm to correctly identify plume gases is directly related to the concentration of the gas. Previous concerns that the algorithm is hindered by spectral overlap were eliminated through the use of constraints on the regression

A Hybrid Thermal Video and FTIR Spectrometer System for Rapidly Locating and Characterizing Gas Leaks

Undiscovered gas leaks, known as fugitive emissions, in chemical plants and refinery operations can impact regional air quality and present a loss of product for industry. Surveying a facility for potential gas leaks can be a daunting task. Industrial leak detection and repair programs can be expensive to administer. An efficient, accurate and cost effective method for detecting and quantifying gas leaks would both save industries money by identifying production losses and improve regional air quality. Specialized thermal video systems have proven effective in rapidly locating gas leaks. These systems, however, do not have the spectral resolution for compound identification. Passive FTIR spectrometers can be used for gas compound identification, but using these systems for facility surveys is problematic due to their small field of view. A hybrid approach has been developed that utilizes the thermal video system to locate gas plumes using real time visualization of the leaks, coupled with the high spectral resolution FTIR spectrometer for compound identification and quantification. The prototype hybrid video/spectrometer system uses a sterling cooled thermal camera, operating in the MWIR (3-5 µm) with an additional notch filter set at around 3.4 µm, which allows for the visualization of gas compounds that absorb in this narrow spectral range, such as alkane hydrocarbons. This camera is positioned alongside of a portable, high speed passive FTIR spectrometer, which has a spectral range of 2 – 25 µm and operates at 4 cm-1 resolution. This system uses a 10 cm telescope foreoptic with an onboard blackbody for calibration. The two units are optically aligned using a turning mirror on the spectrometer’s telescope with the video camera’s output

Gas Plume Species Identification by Regression Analyses

Identification of constituent gases in effluent plumes is performed using linear least-squares regression techniques. Overhead thermal hyperspectral imagery is used for this study. Synthetic imagery is employed as the test-case for algorithm development. Synthetic images are generated by the Digital Imaging and Remote Sensing Image Generation (DIRSIG) Model. The use of synthetic data provides a direct measure of the success of the algorithm through the comparison with truth map outputs. In image test-cases, plumes emanating from factory stacks will have been identified using a separate detection algorithm. The gas identification algorithm being developed in this work will then be used only on pixels having been determined to contain the plume. Stepwise linear regression is considered in this study. Stepwise regression is attractive for this application as only those gases truly in the plume will be present in the final model. Preliminary results from the study show that stepwise regression is successful at correctly identifying the gases present in a plume. Analysis of the results indicates that the spectral overlap of absorption features in different gas species leads to false identifications

Hybridization of Hyperspectral Imaging Target Detection Algorithm Chains

Detection of a known target in an image can be accomplished using several different approaches. The complexity and number of steps involved in the target detection process makes a comparison of the different possible algorithm chains desirable. Of the different steps involved, some have a more significant impact than others on the final result - the ability to find a target in an image. These more important steps often include atmospheric compensation, noise and dimensionality reduction, background characterization, and detection (matched filtering for this research). A brief overview of the algorithms to be compared for each step will be presented. This research seeks to identify the most effective set of algorithms for a particular image or target type. Several different algorithms for each step will be presented, to include ELM, FLAASH, MNF, PPI, MAXD, the structured background matched filters OSP, and ASD. The chains generated by these algorithms will be compared using the Forest Radiance I HYDICE data set. Finally, receiver operating characteristic (ROC) curves will be calculated for each algorithm chain and, as an end result, a comparison of the various algorithm chains will be presented

The Invariant Algorithm for Identification and Detection of Multiple Gas Plumes and Weak Releases

The ability to detect and identify gaseous effluents is a problem that has been pursued with limited success. It has been shown to bepossible using the Invariant algorithm on synthetic hyperspectralscenes with a strong single gas release. That however, is a veryspecific case and leaves room for further investigation. This studylooks at more realistic detection and release scenarios. Ourimplementation of the Invariant algorithm uses Singular ValueDecomposition (SVD) to select basis vectors from a subspace of targetgases in conjunction with a Generalized Likelihood Ratio Test (GLRT) to determine on a pixel by pixel basis how ``like the target gas each pixel is. The target gases are modeled in the image radiance space including atmospheric effects. Target spectra are modeled in both emission and absorption. This study investigates how well weak plumes are detected. Also, there will be a test of a mixed gas in a strong plume release. Finally, a situation where a weak multiple gas release will be discussed. Synthetic hyperspectral imagery in the long wave infrared region (LWIR) of the electromagnetic spectrum will be the predominate data used in this study. This algorithm has been found to be applicable for these detection and identification scenarios

Identification and Detection of Gaseous Effluents from Hyperspectral Imagery Using Invariant Algorithms

The ability to detect and identify effluent gases is, and will continue to be, of great importance. This would not only aid in the regulation of pollutants but also in treaty enforcement and monitoring the production of weapons. Considering these applications, finding a way to remotely investigate a gaseous emission is highly desirable. This research utilizes hyperspectral imagery in the infrared region of the electromagnetic spectrum to evaluate an invariant method of detecting and identifying gases within a scene. The image is evaluated on a pixel-by-pixel basis and is studied at the subpixel level. A library of target gas spectra is generated using a simple slab radiance model. This results in a more robust description of gas spectra which are representative of real-world observations. This library is the subspace utilized by the detection and identification algorithms. The subspace will be evaluated for the set of basis vectors that best span the subspace. The Lee algorithm will be used to determine the set of basis vectors, which implements the Maximum Distance Method (MaxD). A Generalized Likelihood Ratio Test (GLRT) determines whether or not the pixel contains the target. The target can be either a single species or a combination of gases. Synthetically generated scenes will be used for this research. This work evaluates whether the Lee invariant algorithm will be effective in the gas detection and identification problem

Comparisons Between Spectral Quality Metrics and Analyst Performance in Hyperspectral Target Detection

Quantitative methods to assess or predict the quality of a spectral image continue to be the subject of a number of current research activities. An accepted methodology would be highly desirable for use in data collection tasking or data archive searching in ways analogous to the current prediction of panchromatic image quality through the National Imagery Interpretation Rating Scale (NIIRS) using the General Image Quality Equation (GIQE). A number of approaches to the estimation of quality of a spectral image have been published, but most capture only the performance of automated algorithms applied to the spectral data. One recently introduced metric, however, the General Spectral Utility Metric (GSUM), provides for a framework to combine the performance from the spectral aspects together with the spatial aspects. In particular, this framework allows the metric to capture the utility of a spectral image resulting when the human analyst is included in the process. This is important since nearly all hyperspectral imagery analysis procedures include an analyst. To investigate the relationships between candidate spectral metrics and task performance from volunteer human analysts in conjunction with the automated results, simulated images are generated and processed in a blind test. The performance achieved by the analysts is then compared to predictions made from various spectral quality metrics to determine how well the metrics function. The task selected is one of finding a specific vehicle in a cluttered environment using a detection map produced from the hyperspectral image along with a panchromatic rendition of the image. Various combinations of spatial resolution, number of spectral bands, and signal-to-noise ratios are investigated as part of the effort

Low-dimensional Representations of Hyperspectral Data for Use in CRF-based Classification

Probabilistic graphical models have strong potential for use in hyperspectral image classification. One important class of probabilisitic graphical models is the Conditional Random Field (CRF), which has distinct advantages over traditional Markov Random Fields (MRF), including: no independence assumption is made over the observation, and local and pairwise potential features can be defined with flexibility. Conventional methods for hyperspectral image classification utilize all spectral bands and assign the corresponding raw intensity values into the feature functions in CRFs. These methods, however, require significant computational efforts and yield an ambiguous summary from the data. To mitigate these problems, we propose a novel processing method for hyperspectral image classification by incorporating a lower dimensional representation into the CRFs. In this paper, we use representations based on three types of graph-based dimensionality reduction algorithms: Laplacian Eigemaps (LE), Spatial-Spectral Schroedinger Eigenmaps (SSSE), and Local Linear Embedding (LLE), and we investigate the impact of choice of representation on the subsequent CRF-based classifications

Matched Filter Stochastic Background Characterization for Hyperspectral Target Detection

Algorithms exploiting hyperspectral imagery for target detection have continually evolved to provide improved detection results. Adaptive matched filters can be used to locate spectral targets by modeling scene background as either structured (geometric) with a set of endmembers (basis vectors) or as unstructured (stochastic) with a covariance or correlation matrix. These matrices are often calculated using all available pixels in a data set. In unstructured background research, various techniques for improving upon scene-wide methods have been developed, each involving either the removal of target signatures from the background model or the segmentation of image data into spatial or spectral subsets. Each of these methods increase the detection signal-to-background ratio (SBR) and the multivariate normality (MVN) of the data from which background statistics are calculated, thus increasing separation between target and non-target species in the detection statistic and ultimately improving thresholded target detection results. Such techniques for improved background characterization are widely practiced but not well documented or compared. This paper provides a review and comparison of methods in target exclusion, spatial subsetting and spectral pre-clustering, and introduces a new technique which combines these methods. The analysis provides insight into the merit of employing unstructured background characterization techniques, as well as limitations for their practical application