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Methods for Improving Image Quality and Reducing Data Load of NIR Hyperspectral Images

View Article: PubMed Central

ABSTRACT

Near Infrared Hyperspectral Imaging (NIRHSI) is an emerging technology platform that integrates conventional imaging and spectroscopy to attain both spatial and spectral information from an object. Two important problems in NIRHSI are those of data load and unserviceable pixels in the NIR sensor. Hyperspectral imaging experiments generate large amounts of data (typically > 50 MB per image), which tend to overwhelm the memory capacity of conventional computer systems. This inhibits the utilisation of NIRHSI for routine online industrial application. In general, approximately 1% of pixels in NIR detectors are unserviceable or ‘dead’, containing no useful information. While this percentage of pixels is insignificant for single wavelength imaging, the problem is amplified in NIRHSI, where > 100 wavelength images are typically acquired. This paper describes an approach for reducing the data load of hyperspectral experiments by using sample-specific vector-to-scalar operators for real time feature extraction and a systematic procedure for compensating for ‘dead’ pixels in the NIR sensor. The feasibility of this approach was tested for prediction of moisture content in carrot tissue.

No MeSH data available.


Conversion of 2-D spatial-spectral matrix, I(x,λ) into score matrix, T(x), as a result of a vector multiplication operator.
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f5-sensors-08-03287: Conversion of 2-D spatial-spectral matrix, I(x,λ) into score matrix, T(x), as a result of a vector multiplication operator.

Mentions: To perform real time feature extraction of hyperspectral images, the vector-to-scalar operator of the desired feature, Op(λ), must first be estimated by experimentation. An example of how this may be done is given further on in this paper. Once this has been done, while scanning the frame of an examined line, the 2-D (spatial vs spectral) Intensity matrix, I(x, λ), is real-time pre-processed into a score vector, T(x), by multiplying the spectrum of each pixel by Op(λ) (see Figure 5). By moving the object under the camera (in the y-direction) and grabbing the frames of subsequent lines, the scores matrix of features, T(x,y), is built. This matrix may be displayed while scanning as a pseudo-image, showing the distribution of the selected feature on the surface. Selected score matrices, which are substantially smaller in size than the corresponding hypercube (3-4 score image planes as opposed to >100 spectral planes in the hypercube) may then be saved for further analysis by usual image processing methods.


Methods for Improving Image Quality and Reducing Data Load of NIR Hyperspectral Images
Conversion of 2-D spatial-spectral matrix, I(x,λ) into score matrix, T(x), as a result of a vector multiplication operator.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3675544&req=5

f5-sensors-08-03287: Conversion of 2-D spatial-spectral matrix, I(x,λ) into score matrix, T(x), as a result of a vector multiplication operator.
Mentions: To perform real time feature extraction of hyperspectral images, the vector-to-scalar operator of the desired feature, Op(λ), must first be estimated by experimentation. An example of how this may be done is given further on in this paper. Once this has been done, while scanning the frame of an examined line, the 2-D (spatial vs spectral) Intensity matrix, I(x, λ), is real-time pre-processed into a score vector, T(x), by multiplying the spectrum of each pixel by Op(λ) (see Figure 5). By moving the object under the camera (in the y-direction) and grabbing the frames of subsequent lines, the scores matrix of features, T(x,y), is built. This matrix may be displayed while scanning as a pseudo-image, showing the distribution of the selected feature on the surface. Selected score matrices, which are substantially smaller in size than the corresponding hypercube (3-4 score image planes as opposed to >100 spectral planes in the hypercube) may then be saved for further analysis by usual image processing methods.

View Article: PubMed Central

ABSTRACT

Near Infrared Hyperspectral Imaging (NIRHSI) is an emerging technology platform that integrates conventional imaging and spectroscopy to attain both spatial and spectral information from an object. Two important problems in NIRHSI are those of data load and unserviceable pixels in the NIR sensor. Hyperspectral imaging experiments generate large amounts of data (typically > 50 MB per image), which tend to overwhelm the memory capacity of conventional computer systems. This inhibits the utilisation of NIRHSI for routine online industrial application. In general, approximately 1% of pixels in NIR detectors are unserviceable or ‘dead’, containing no useful information. While this percentage of pixels is insignificant for single wavelength imaging, the problem is amplified in NIRHSI, where > 100 wavelength images are typically acquired. This paper describes an approach for reducing the data load of hyperspectral experiments by using sample-specific vector-to-scalar operators for real time feature extraction and a systematic procedure for compensating for ‘dead’ pixels in the NIR sensor. The feasibility of this approach was tested for prediction of moisture content in carrot tissue.

No MeSH data available.