SLCCOR

Description

SLCCOR shifts the band center wavelength value and FWHM values of a multiband at-sensor radiance data set or a multiband surface reflectance data set as a function of along-scanline position and band index, and convolves the band spectra with the data set's nominal relative response profile. The wavelength and FWHM shifts are obtained from a correction lookup table (CLUT) and are relative to the data set's nominal relative response profile. When the shifts represent actual variation of the sensor relative response profile over the data set, this operation makes the single nominal relative response profile valid over the entire data set. This program is intended to be used with a CLUT generated by GENCLUT so that it can remove relative response profile variation due to spectral line curvature.

Parameters

Name	Type	Length	Value range
InputCLUT: Input CLUT segment *	BIN port	1 - 1
Input: Input image channels *	Raster port	1 - 1024
Output *	Integer	1 - 1024
Valid Bands Only	String	0 - 1	YES | NO Default: NO

* Required parameter

Parameter descriptions

InputCLUT: Input CLUT segment

Specifies the input segment that contains the spectral line curvature lookup table (CLUT).

Input: Input image channels

Specifies the input channels that contain the hyperspectral database bands.

Output

Specifies the output channel to receive the transformed image data.

Valid Bands Only

Specifies whether the selected bands are to be restricted to those with 'plot' or 'bmask' (begin mask) quality values.

This parameter has no effect if the input file contains no band-validity metadata.

Details

Spectral line curvature (also called "spectral smile") is a feature of image data acquired with a pushbroom-type imaging spectrometer employing a two-dimensional detector array. (This is a common design for imaging spectrometers.) When an image that exhibits spectral line curvature is atmospherically corrected using a method that relies on accurate center-wavelength and FWHM data for the image bands, the along-band dimension of the result may exhibit noise (spiking) in the neighborhood of absorption bands.

If the amount of such noise is small enough, it may be removed effectively enough for pratical purposes by smoothing (see SMSPEC). In many cases, however, the amount of noise is so large that the degree of smoothing required to remove it will distort the data. It is often preferable to reduce spectral line curvature from the data before performing atmospheric correction, to reduce the amount of smoothing required afterwards.

Spectral line curvature occurs when the image of the spectrometer entrance slit within a particular waveband does not fall entirely on the detector array row that corresponds to that waveband. The slit-image may curve (or "smile") so that it intersects multiple detector array rows. In general, spectral line curvature affects all wavebands of an image to a varying degree.

A correction for spectral line curvature is based on shifts to the nominal center-wavelength and FWHM values associated with the image data that is a function of image line coordinate (the image line dimension maps to the detector array row dimension) and band. GENCLUT can be used to generate a two-dimensional spectral line curvature lookup table (CLUT) that contains shifts that represent the effect of spectral line curvature. The CLUT can then be used in ATRLUT to resample the radiance spectra from the radiance lookup table (RLUT) so that they more closely match the actual (distorted) sensor relative response profiles for different band and across-scanline positions. SLCCOR can then be used to convolve the reflectance data set with the nominal relative response profile so that the profile will be valid over the entire data set.

Alternatively, the radiance data set can be convolved with the nominal relative response profile to remove the spectral line curvature before running ATRLUT. In this case, the CLUT is not input to ATRLUT.

Algorithm

SLCCOR relies on the existence of a spectral smile curvature lookup table (CLUT) containing the relevant center wavelength and FWHM shifts that need to be applied to correct a data set.

Spectral line curvature correction is achieved by convolving the input spectra to the shifted band response profiles as defined by the CLUT.

The CLUT contains a single center wavelength shift and FWHM shift for each crosstrack pixel location/spectral band combination. Accordingly, every along-track spectrum for a fixed cross-track pixel location will be convolved to the same shifted band response profiles.

If only valid bands were selected, only the bands marked with "plot" or "bmask" will be convolved and output.

There are two possible approaches for performing "smile" correction coupled with a radiance to reflectance atmospheric transformation for a given hyperspectral image data set. Both approaches require the creation of an at-sensor radiance lookup table and the extraction of a vertical water vapor content map for the entire image. They differ, however, in the manner in which the "smile" correction and the atmospheric correction are applied.

CLUT format

The spectral line curvature correction lookup table (CLUT) is a two-dimensional array.

The dimensional sizes of a CLUT are determined from the input data set and the input sensor. The crosstrack dimension contains a CLUT entry for every data set crosstrack pixel. The band dimension contains a CLUT entry for every band, as identified by the sensor.

A CLUT is stored exclusively in a PCIDSK binary segment. One CLUT can be stored per segment. All CLUT attributes are saved as metadata for that binary segment.

When the CLUT is saved to disk, the CLUT entries are serialized into a system-independent byte stream and written to the binary segment.

Acknowledgements

PCI Geomatics received financial support from the Canadian Space Agency/L'Agence Spatiale Canadienne through the Earth Observation Application Development Program (EOADP) for the development of this software, under contract 9F028-0-4914/09.

References

R.A. Neville, L. Sun, K. Staenz, 2003. "Detection of Spectral Line Curvature In Imaging Spectrometer Data", Proceedings of AeroSense: Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery IX, 21-25 April 2003, Orlando, Florida.