Pseudo-imaging the spectral-temporal evolution of energetic transient events

Weeks, Melanie M.; Murguia, James E.; Mooney, Jonathan Martin; Nelson, R. J.; Ewing, William S.

doi:10.1117/12.583486

Cited by 5 publications

(6 citation statements)

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“…Pseudo-imaging is the process of taking data obtained from the focal plane array, creating a single spectral profile, and using the spectral profiles from multiple 'snapshots' to create a spectral-temporal signature from an observed event (Weeks et al 2004). Figure 1 shows a schematic of the sensor we used from the work of Mooney et al (1997) and Pellegrini and Ewing (2004).…”

Section: Datamentioning

confidence: 99%

Modeling Spectral–Temporal Data From Point Source Events

et al. 2011

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In recent years, a great deal of effort has been invested in developing sensors to detect, locate, and identify "energetic" electromagnetic events. When observed through one type of imaging spectrometer, these events produce a data record that contains complete spectral and temporal information over the event's evolution. This article describes the development of a statistical model for the data produced by a particular spectral-temporal sensor. While the application is unique in some ways, this approach to model building may be useful in other related contexts. Several plots, estimated parameters, and some additional details for an equation are provided in the Appendix which is available as supplementary material online.

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Section: Datamentioning

confidence: 99%

Modeling Spectral–Temporal Data From Point Source Events

et al. 2011

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“…Thus, the spectrum and location of energetic point-like objects can be determined without the formation of an image. Further description of CTHIS characteristics and operation can be found in [8] and [11].…”

Section: Definitionmentioning

confidence: 99%

“…11 Such an architecture might resemble the slitless spectrometer, except that point sources rather than line sources are the targets of interest and the sensor must acquire the information necessary to extract the spectrum and event location without additional information. To be specific, consider the following formal definition for a new class of sensors:…”

Section: Pseudo Imagingmentioning

confidence: 99%

Pseudo imaging

2006

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The detection, determination of location, and identification of unknown and uncued energetic events within a large field of view represents a common operational requirement for many staring sensors. The traditional imaging approach involves forming an image of an extended scene and then rejecting background clutter. However, some important targets can be limited to a class of energetic, transient, point-like events, such as explosions, that embed key discriminants within their emitted, temporally varying spectra; for such events it is possible to create an alternative sensor architecture tuned specifically to these objects of interest. The resulting sensor operation, called pseudo imaging, includes: optical components designed to encode the scene information such that the spectral-temporal signature from the event and its location are easily derived; and signal processing intrinsic to the sensor to declare the presence of an event, locate the event, extract the event spectral-temporal signature, and match the signature to a library in order to identify the event.This treatise defines pseudo imaging, including formal specifications and requirements. Two examples of pseudo imaging sensors are presented: a sensor based on a spinning prism, and a sensor based on an optical element called a Crossed Dispersion Prism. The sensors are described, including how the sensors fulfill the definition of pseudo imaging, and measured data is presented to demonstrate functionality.

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“…Pseudo-imaging is normally performed as follows (please refer to references [2], [5]). Given a known optical design, with a specified undeviated wavelength, if we rotate the prism, subtracting off background frames before the event began in a rotationally sensitive manner, the line spectrum will appear to spin around its undeviated wavelength.…”

Section: Rotating Spectrometer Systemmentioning

confidence: 99%

“…One can exploit the dynamic nature of the event by recording a background image frame before an event begins, and then using this as a reference to subtract from future frames. Assuming the rate of change of the background is small compared to the sampling interval, the FPA will record the time evolution of the emitted spectra of the event of interest, a technique known as pseudo-imaging [2]. The rich spectral-temporal information provided by pseudo-imaging can then be used to help identify the source.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Estimation for Spectral-Temporal Characterization of Energetic Transient Events

Deming¹,

Higbee

Dwyer

et al. 2006

The 2006 IEEE International Joint Conference on Neural Network Proceedings

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We describe a new approach for performing pseudo-imaging of point energy sources from spectral-temporal sensor data. Pseudo-imaging, which involves the automatic localization, spectrum estimation, and identification of energetic sources, can be difficult for dim sources and/or noisy images, or in data containing multiple sources which are closely spaced such that their signatures overlap. The new approach is specifically designed for these difficult cases. It is developed within the framework of modeling field theory (MFT), a biologically-inspired neural network system that has demonstrated practical value in many diverse areas. MFT performs an efficient optimization over the space of all model parameters and mappings between image pixels and sources, or clutter. The optimized set of parameters is then used for detection, localization and identification of the multiple sources in the data. The paper includes results computed from experimental spectrometer data.

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Pseudo-imaging the spectral-temporal evolution of energetic transient events

Cited by 5 publications

References 0 publications

Modeling Spectral–Temporal Data From Point Source Events

Modeling Spectral–Temporal Data From Point Source Events

Pseudo imaging

Adaptive Estimation for Spectral-Temporal Characterization of Energetic Transient Events

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