Singular spectrum analysis : a note on data processing for Fourier transform hyperspectral imagers

Hyperspectral remote sensing is experiencing a dazzling proliferation of new sensors, platforms, sys tems, and applications with the introduction of novel, low cost, low weight sensors. Curiously, relatively little development is now occurring in the use of Fourier Transform (FT) systems, which have the potential to operate at extremely high throughput wi thout use of a slit or reductions in both spatial and spectral resolution that thin film based mosaic sensors introduce. This study introduces a new physics - based analytical framework called Singular Spectrum Analysis (SSA) to process raw hyperspectral ima gery collected with FT imagers that addresses some of the data processing issues associated with FT instruments including the need to remove low frequency variations in the interferogram that are introduced by the optical system, as well as high frequency variations that lay outside the detector band pass. Synthetic interferogram data is analyzed using SSA, which adaptively decomposes the original synthetic interferogram into several independent components associated with the signal, photon and system nois e, and the field illumination pattern

Hyperspectral Applications in the Global Transportation Infrastructure

Raj Bridgelall is the program director for the Upper Great Plains Transportation Institute (UGPTI) Center for Surface Mobility Applications & Real-time Simulation environments (SMARTSeSM).Hyperspectral remote sensing is an emerging field with potential applications in the observation, management, and maintenance of the global transportation infrastructure. This study introduces a general analytical framework to link transportation systems analysis and hyperspectral analysis. The authors introduce a range of applications that would benefit from the capabilities of hyperspectral remote sensing. They selected three critical but unrelated applications and identified both the spatial and spectral information of their key operational characteristics to demonstrate the hyperspectral utility. The specific scenario studies exemplifies the general approach of utilizing the outputs of hyperspectral analysis to improve models that practitioners currently use to analyze a variety of transportation problems including roadway congestion forecasting, railway condition monitoring, and pipeline risk management.Mountain Plains Consortium (MPC)https://www.ugpti.org/about/staff/viewbio.php?id=7

Imaging Fourier transform spectrometers for environmental sensing

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/77073/1/AIAA-1998-291-523.pd

Towards a Continuous Record of the Sky

It is currently feasible to start a continuous digital record of the entire sky sensitive to any visual magnitude brighter than 15 each night. Such a record could be created with a modest array of small telescopes, which collectively generate no more than a few Gigabytes of data daily. Alternatively, a few small telescopes could continually re-point to scan and reco rd the entire sky down to any visual magnitude brighter than 15 with a recurrence epoch of at most a few weeks, again always generating less than one Gigabyte of data each night. These estimates derive from CCD ability and budgets typical of university research projects. As a prototype, we have developed and are utilizing an inexpensive single-telescope system that obtains optical data from about 1500 square degrees. We discuss the general case of creating and storing data from a both an epochal survey, where a small number of telescopes continually scan the sky, and a continuous survey, composed of a constellation of telescopes dedicated each continually inspect a designated section of the sky. We compute specific limitations of canonical surveys in visible light, and estimate that all-sky continuous visual light surveys could be sensitive to magnitude 20 in a single night by about 2010. Possible scientific returns of continuous and epochal sky surveys include continued monitoring of most known variable stars, establishing case histories for variables of future interest, uncovering new forms of stellar variability, discovering the brightest cases of microlensing, discovering new novae and supernovae, discovering new counterparts to gamma-ray bursts, monitoring known Solar System objects, discovering new Solar System objects, and discovering objects that might strike the Earth.Comment: 38 pages, 9 postscript figures, 2 gif images. Revised and new section added. Accepted to PASP. Source code submitted to ASCL.ne

Physically motivated correlation formalism in hyperspectral imaging

Most remote sensing data-sets contain a limiting number of independent spatial and spectral measurements, beyond which no effective increase in information is achieved. This paper presents a Physically Motivated Correlation Formalism (PMCF),which places both Spatial and Spectral data on an equivalent mathematical footing in the context of a specific Kernel, such that, optimal combinations of independent data can be selected from the entire Hypercube via the method of \u27Correlation Moments\u27. We present an experimental and computational analysis of Hyperspectral data sets using the Michigan Tech VFTHSI [Visible Fourier Transform Hyperspectral Imager] based on a Sagnac Interferometer, adjusted to obtain high SNR levels. The captured Signal Interferograms of different targets - aerial snaps of Houghton and lab-based data (white light, He-Ne laser, discharge tube sources) with the provision of customized scan of targets with the same exposures are processed using inverse imaging transformations and filtering techniques to obtain the Spectral profiles and generate Hypercubes to compute Spectral/Spatial/Cross Moments. PMCF answers the question of how optimally the entire hypercube should be sampled and finds how many spatial-spectral pixels are required for a particular target recognition

Hyperspectral Imaging Utility for Transportation Systems

Raj Bridgelall is the program director for the Upper Great Plains Transportation Institute (UGPTI) Center for Surface Mobility Applications & Real-time Simulation environments (SMARTSeSM).The global transportation system is massive, open, and dynamic. Existing performance and condition assessments of the complex interacting networks of roadways, bridges, railroads, pipelines, waterways, airways, and intermodal ports are expensive. Hyperspectral imaging is an emerging remote sensing technique for the non-destructive evaluation of multimodal transportation infrastructure. Unlike panchromatic, color, and infrared imaging, each layer of a hyperspectral image pixel records reflectance intensity from one of dozens or hundreds of relatively narrow wavelength bands that span a broad range of the electromagnetic spectrum. Hence, every pixel of a hyperspectral scene provides a unique spectral signature that offers new opportunities for informed decision-making in transportation systems development, operations, and maintenance. Spaceborne systems capture images of vast areas in a short period but provide lower spatial resolution than airborne systems. Practitioners use manned aircraft to achieve higher spatial and spectral resolution, but at the price of custom missions and narrow focus. The rapid size and cost reduction of unmanned aircraft systems promise a third alternative that offers hybrid benefits at affordable prices by conducting multiple parallel missions. This research formulates a theoretical framework for a pushbroom type of hyperspectral imaging system on each type of data acquisition platform. The study then applies the framework to assess the relative potential utility of hyperspectral imaging for previously proposed remote sensing applications in transportation. The authors also introduce and suggest new potential applications of hyperspectral imaging in transportation asset management, network performance evaluation, and risk assessments to enable effective and objective decision- and policy-making.https://www.ugpti.org/about/staff/viewbio.php?id=7

Hyperspectral Range Imaging for Transportation Systems Evaluation

Raj Bridgelall is the program director for the Upper Great Plains Transportation Institute (UGPTI) Center for Surface Mobility Applications & Real-time Simulation environments (SMARTSeSM).Transportation agencies expend significant resources to inspect critical infrastructure such as roadways, railways, and pipelines. Regular inspections identify important defects and generate data to forecast maintenance needs. However, cost and practical limitations prevent the scaling of current inspection methods beyond relatively small portions of the network. Consequently, existing approaches fail to discover many high-risk defect formations. Remote sensing techniques offer the potential for more rapid and extensive non-destructive evaluations of the multimodal transportation infrastructure. However, optical occlusions and limitations in the spatial resolution of typical airborne and spaceborne platforms limit their applicability. This research proposes hyperspectral image classification to isolate transportation infrastructure targets for high-resolution photogrammetric analysis. A plenoptic swarm of unmanned aircraft systems will capture images with centimeter-scale spatial resolution, large swaths, and polarization diversity. The light field solution will incorporate structure-from-motion techniques to reconstruct three-dimensional details of the isolated targets from sequences of two-dimensional images. A comparative analysis of existing low-power wireless communications standards suggests an application dependent tradeoff in selecting the best-suited link to coordinate swarming operations. This study further produced a taxonomy of specific roadway and railway defects, distress symptoms, and other anomalies that the proposed plenoptic swarm sensing system would identify and characterize to estimate risk levels.Mountain Plains Consortium (MPC)https://www.ugpti.org/about/staff/viewbio.php?id=7

A review of hyperspectral imagers and comparison with respect to real time processing on space and aircraft platforms

© 1998 SPIE. All rights reserved. Over the last decade, various designs for hyperspectral instruments have been developed and may be categorized roughly by the way in which they acquire hyperspectral data: via filter, dispersion (grating or prism), or Fourier transform. Each category has unique characteristics that lead to differing processing needs. Fueled by increasing demands for real time hyperspectral data from space and aircraft platforms, a new generation of data processing capabilities are being developed by an increasingly large community with the objective of accommodating the high data rate produced by these hyperspectral imagers (HSIs). This paper provides an overview of the three basic categories of HSIs and then contrasts each with respect to current and planned processing capabilities

THRIFTI: Tomographic hyperspectral remote imaging fourier transform interferometer

Hyperspectral imaging spectrometers (HSIs) utilizing a Sagnac interferometer are throughput-limited if a slit is employed in their designs. This paper describes the Tomographic Hyperspectral Remote Imaging Fourier Transform Interferometer (THRIFTI) optical design. THRIFTI is capable of producing spectral autocorrelation fringe modulation over an image plane defined by a two-dimensional CCD array without the throughput disadvantage encountered by the Sagnac-based imaging spectrometers that incorporate a slit. This approach is utilized to recapture the full spatial-spectral characteristics of an image hypercube via tomography or linear deconvolution. In addition to its large throughput, THRIFTI is robust and simple to construct. The optical design of THRIFTI is discussed and the first experimental results are presented