Airborne hyperspectral sensor systems

Birk, Ronald J.; McCord, T. B.

doi:10.1109/62.318881

Cited by 26 publications

(6 citation statements)

References 7 publications

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“…As most imaging spectrometers critically sample spectra, their spectral resolution would be twice their BP width by this definition. For more information on existing hyperspectral systems, see Birk and McCord [1994], Clark [1999], and Kruse [1999].…”

Section: Introductionmentioning

confidence: 99%

Effects of spectrometer band pass, sampling, and signal‐to‐noise ratio on spectral identification using the Tetracorder algorithm

et al. 2003

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[1] Estimates of spectrometer band pass, sampling interval, and signal-to-noise ratio required for identification of pure minerals and plants were derived using reflectance spectra convolved to AVIRIS, HYDICE, MIVIS, VIMS, and other imaging spectrometers. For each spectral simulation, various levels of random noise were added to the reflectance spectra after convolution, and then each was analyzed with the Tetracorder spectral identification algorithm . The outcome of each identification attempt was tabulated to provide an estimate of the signal-to-noise ratio at which a given percentage of the noisy spectra were identified correctly. Results show that spectral identification is most sensitive to the signal-to-noise ratio at narrow sampling interval values but is more sensitive to the sampling interval itself at broad sampling interval values because of spectral aliasing, a condition when absorption features of different materials can resemble one another. The band pass is less critical to spectral identification than the sampling interval or signal-to-noise ratio because broadening the band pass does not induce spectral aliasing. These conclusions are empirically corroborated by analysis of mineral maps of AVIRIS data collected at Cuprite, Nevada, between 1990 and 1995, a period during which the sensor signal-to-noise ratio increased up to sixfold. There are values of spectrometer sampling and band pass beyond which spectral identification of materials will require an abrupt increase in sensor signal-to-noise ratio due to the effects of spectral aliasing. Factors that control this threshold are the uniqueness of a material's diagnostic absorptions in terms of shape and wavelength isolation, and the spectral diversity of the materials found in nature and in the spectral library used for comparison. Array spectrometers provide the best data for identification when they critically sample spectra. The sampling interval should not be broadened to increase the signal-to-noise ratio in a photon-noise-limited system when high levels of accuracy are desired. It is possible, using this simulation method, to select optimum combinations of band-pass, sampling interval, and signal-to-noise ratio values for a particular application that maximize identification accuracy and minimize the volume of imaging data.

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Section: Introductionmentioning

confidence: 99%

Effects of spectrometer band pass, sampling, and signal‐to‐noise ratio on spectral identification using the Tetracorder algorithm

et al. 2003

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“…This type of approach, in which many channels (often hundreds), typically of reflectance spectra, are collected simultaneously has been developed in particular by the remote-sensing community, where, when coupled increasingly to advanced data reduction and visualization algorithms and combined with spatial discrimination, it is known as imaging spectrometry (207,360,361) or hyperspectral imaging (1,91,162,310,335,361,549,812,1034). The discrimination afforded by the hyperspectral approach over the more traditional multispectral approach in which only a few wavelength channels are collected is predictably (Fig.…”

Section: Some Prospectsmentioning

confidence: 99%

Flow cytometry and cell sorting of heterogeneous microbial populations: the importance of single-cell analyses.

Davey

Kell

1996

Microbiological Reviews

556

297

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“…Figure 4 illustrates two methods of capturing spectral information ͑ axis͒ with the spatial image. 11,12 The first collection scheme ͓Fig. 4͑a͔͒ employs a spinning mirror to sample the ground image across a row that is perpendicular to the ground track.…”

Section: B Store-and-process Systemsmentioning

confidence: 99%

“…However, this amount is still considerably less than the traditional store-and-process data-storage requirements of 10 Gbytes for AVIRIS image formats. 12 The performance of the SIMD focal-plane system for the different applications is compared with a 4-Gop͞s store-and-process DSP chip running at 500 MHz. Following trends in architecture evolution, the DSP system performance is based on an instruction-percycle throughput of 8 ͑compared with today's microprocessor's instruction per cycle of 2 to 3͒.…”

Section: B Computation Throughputmentioning

confidence: 99%

Focal-plane processing architectures for real-time hyperspectral image processing

et al. 2000

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Real-time image processing requires high computational and I/O throughputs obtained by use of optoelectronic system solutions. A novel architecture that uses focal-plane optoelectronic-area I/O with a fine-grain, low-memory, single-instruction-multiple-data (SIMD) processor array is presented as an efficient computational solution for real-time hyperspectral image processing. The architecture is evaluated by use of realistic workloads to determine data throughputs, processing demands, and storage requirements. We show that traditional store-and-process system performance is inadequate for this application domain, whereas the focal-plane SIMD architecture is capable of supporting real-time performances with sustained operation throughputs of 500-1500 gigaoperations/s. The focal-plane architecture exploits the direct coupling between sensor and parallel-processor arrays to alleviate data-bandwidth requirements, allowing computation to be performed in a stream-parallel computation model, while data arrive from the sensors.

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Airborne hyperspectral sensor systems

Cited by 26 publications

References 7 publications

Effects of spectrometer band pass, sampling, and signal‐to‐noise ratio on spectral identification using the Tetracorder algorithm

Effects of spectrometer band pass, sampling, and signal‐to‐noise ratio on spectral identification using the Tetracorder algorithm

Flow cytometry and cell sorting of heterogeneous microbial populations: the importance of single-cell analyses.

Focal-plane processing architectures for real-time hyperspectral image processing

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