Nikunj C. Oza's Publications

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Improvements in Virtual Sensors: Using Spatial Information to Estimate Remote Sensing Spectra

Improvements in Virtual Sensors: Using Spatial Information to Estimate Remote Sensing Spectra. Nikunj C. Oza, Ashok N. Srivastava, and Julienne Stroeve. In Proceedings of the International Geoscience and Remote Sensing Symposium, Institute for Electrical and Electronics Engineers, New Jersey, July 2005.

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Abstract

Various instruments are used to create images of the Earth and otherobjects in the universe in a diverse set of wavelength bands with theaim of understanding natural phenomena. Sometimes these instrumentsare built in a phased approach, with additional measurementcapabilities added in later phases. In other cases, technology maymature to the point that the instrument offers new measurementcapabilities that were not planned in the original design of theinstrument. In still other cases, high resolution spectralmeasurements may be too costly to perform on a large sample andtherefore lower resolution spectral instruments are used to take themajority of measurements. Many applied science questions that arerelevant to the earth science remote sensing community requireanalysis of enormous amounts of data that were generated byinstruments with disparate measurement capabilities. In past work \citesroz05, we addressed this problem using Virtual Sensors: a method that uses modelstrained on spectrally rich (high spectral resolution) data to "fillin" unmeasured spectral channels in spectrally poor (low spectralresolution) data. We demonstrated this method by using models trained on the high spectral resolution Terra MODIS instrument to estimate what theequivalent of the MODIS 1.6 micron channel would be for the NOAAAVHRR/2 instrument. The scientific motivation for the simulation ofthe 1.6 micron channel is to improve the ability of the AVHRR/2 sensorto detect clouds over snow and ice. This work contains preliminary experiments demonstrating that the use of spatial information can improve our ability to estimate these spectra.

BibTeX Entry

@inproceedings{ozsr05,
	author={Nikunj C. Oza, Ashok N. Srivastava, and Julienne Stroeve},
	title={Improvements in Virtual Sensors: Using Spatial Information to Estimate Remote Sensing Spectra},
	booktitle={Proceedings of the International Geoscience and Remote Sensing Symposium},
	publisher={Institute for Electrical and Electronics Engineers},
	address={New Jersey},
	month={July},
abstract={Various instruments are used to create images of the Earth and other
objects in the universe in a diverse set of wavelength bands with the
aim of understanding natural phenomena.  Sometimes these instruments
are built in a phased approach, with additional measurement
capabilities added in later phases.  In other cases, technology may
mature to the point that the instrument offers new measurement
capabilities that were not planned in the original design of the
instrument. In still other cases, high resolution spectral
measurements may be too costly to perform on a large sample and
therefore lower resolution spectral instruments are used to take the
majority of measurements.  Many applied science questions that are
relevant to the earth science remote sensing community require
analysis of enormous amounts of data that were generated by
instruments with disparate measurement capabilities.  In past work~\cite{sroz05}, we addressed this problem using Virtual Sensors: a method that uses models
trained on spectrally rich (high spectral resolution) data to "fill
in" unmeasured spectral channels in spectrally poor (low spectral
resolution) data. We demonstrated this method by using models trained on the high spectral resolution Terra MODIS instrument to estimate what the
equivalent of the MODIS 1.6 micron channel would be for the NOAA
AVHRR/2 instrument.  The scientific motivation for the simulation of
the 1.6 micron channel is to improve the ability of the AVHRR/2 sensor
to detect clouds over snow and ice. This work contains preliminary experiments demonstrating that the use of spatial information can improve our ability to estimate these spectra.},
	bib2html_pubtype={Refereed Conference},
	bib2html_rescat={Remote Sensing},
	year={2005}
}

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