NDLI: Impact of Sensor Signal-to-Noise Ratio and Spectral Characteristics on Hyperspectral Geoscience Products

Content Provider	IEEE Xplore Digital Library
Author	Sun, L. Staenz, K. Neville, R.A. White, H.P.
Copyright Year	2006
Description	Author affiliation: Natural Resources Canada, Canada Center for Remote Sensing, Ottawa, ON (Sun, L.; Staenz, K.; Neville, R.A.; White, H.P.)
Abstract	Trade-off studies are essential in designing and developing any remote sensing instrument. In order to make effective decisions, the sensitivity analysis of task-specific information to be extracted from the remotely sensed data to the sensor's characteristic parameters must be conducted based on various simulated data. The impact of the changes of signal-to-noise ratio (SNR), spectral sampling interval (SSI), wavelength center position (WCP), and wavelength center error (WCE), on the identification and unmixing fraction maps of fifteen selected pure minerals is investigated in this paper. The results show that, for a sensor with a typical responsivity spectral dependence, a specification of SNR = 200 at 2100 nm is insufficient for identifying and mapping the selected minerals with linear spectral unmixing using the SWIR II region (1950 to 2450 nm). On the other hand, if the SNR is high enough, a SSI of 8.2 nm is sufficient for producing these geoscience products no matter where the WCPs of a sensor's bands are located. It was found that some minerals, such as dickite, become unidentifiable when the SSI is increased to > 12.3 nm. A WCE has impacts on both the identification and unmixing fractions of some minerals, but these are small relative to the impacts of SNR.
Starting Page	2064
Ending Page	2067
File Size	440375
Page Count	4
File Format	PDF
ISBN	0780395107
DOI	10.1109/IGARSS.2006.534
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-07-31
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hyperspectral sensors Sensor phenomena and characterization Signal to noise ratio Geoscience Minerals Hyperspectral imaging Remote sensing Instruments Sensitivity analysis Data mining
Content Type	Text
Resource Type	Article

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