DETECTION AND CORRECTION OF SPECTRAL AND SPATIAL MISREGISTRATIONSFOR HYPERSPECTRAL DATA
Naoto Yokoya,1 Norihide Miyamura,2 and Akira Iwasaki212Department of Aeronautics and Astronautics, University of Tokyo, Japan
Research Center for Advanced Science and Technology, University of Tokyo, Japan∗E-mail: yokoya@sal.rcast.u-tokyo.ac.jp
ABSTRACT

Hyperspectral imaging sensors suffer from spectral and spatial misregistrations. These artifacts prevent the accurate acquisition of the spectra and thus reduce classification accuracy. The main objective of this work is to detect and correct
spectral and spatial misregistrations of hyperspectral images.
The Hyperion visible near-infrared (VNIR) subsystem is used
as an example. An image registration method using normalized cross-correlation for characteristic lines in spectrum image demonstrates its effectiveness for detection of the spectral
and spatial misregistrations. Cubic spline interpolation using
estimated properties makes it possible to modify the spectral
signatures. The accuracy of the proposed postlaunch estimation of the Hyperion properties has been proven to be comparable to that of the prelaunch measurements, which enables
the precise onboard calibration of hyperspectral sensors.
Index Terms— hyperspectral sensor, Hyperion, spectral
misregistration, spatial misregistration
1. INTRODUCTION
Hyperspectral imaging sensors enable high-spectral resolution remote sensing, such as the precise classification of vegetation and mineral mapping. They collect spectrogram as aset of data cube, each of which represents a spectral band radiance. These images form a three-dimensional (3D) cube,
composed of crosstrack (x), line (y) and spectrum (λ) dimensions. Hyperspectral data contains hundreds to thousands ofspectral bands. Owing to the high-resolution spectral profile
in each pixel, it is possible to discriminate among land-coverclasses that are spectrally similar [1].
Hyperspectral imaging sensors usually suffer from spectral and spatial misregistrations. Optical-system aberrations
and misalignments cause these artifacts mainly due to pushbroom systems, where crosstrack and spectral pixels are continuously recorded at the same time using a two-dimensional(2D) detector array. Spectral misregistration, also known asa smile curve, is a shift in wavelength in the spectral domain, which is a function of the crosstrack pixel number. Spatial misregistration, also called keystone , corresponds toband-to-band misregistration. Both types of misregistration
distort the spectral features and thus reduce classification accuracy.
The Hyperion is a hyperspectral imager onboard the EarthObserving 1 (EO-1) satellite launched on November 21,
2000. The Hyperion system, consists of visible near-infrared(VNIR) and short-wave infrared (SWIR) subsystems, andacquires data in 198 spectral bands. The Hyperion VNIR
spectrometer obtains optical images of 50 bands over a rangeof 426.8-925.4 nm. In this work, the Hyperion VNIR isused as an example because the specification of the sensor isdescribed well.
The main objective of this work is to detect and correct thespectral and spatial misregistration of hyperspectral image.
An image registration using normalized cross-correlation forcharacteristic lines in the spectrum image, or the x-λ plane,
is used for detection and cubic spline interpolation is employed for correction of artifacts. First, detection methods
and correction results for spectral and spatial misregistration
are demonstrated, respectively. The change of the alignmentof the sensor components at the launch is then investigated bya simulation of basic spectrometer model.
2. SPECTRAL MISREGISTRATION
Spectral smile becomes observable when the image is transformed into maximum noise fraction (MNF) space [2]. MNF
transformation produces new components ordered by image
quality by projecting the original image into a space where
the new components are sorted in order of SNR. The mostimportant procedure in MNF is to estimate the noise covariance matrix effectively. To get the noise covariance matrix,
we estimate the signal part of each pixel. There are many various models that estimate the value ŝx,y,λ , the signal part oforiginal value sx,y,λ . To visualize the spectral smile clearlyin low-order MNF, we adopt{
asx+1,y,λ (x < W )ŝx,y,λ =(1 ≤ y ≤ H, 1 ≤ λ ≤ N ),asx−1,y,λ (x = W )
(1)

4000

Weighting Factor

(d)

0.02
0.01

Wavelength Shift (pixel)

Radiance (DN)

5000

3000

(a)

(b)

(c)

6000

0.05
0-0.05-0.1-0.15

Pre-launch
Estimated-0.2-0.2500100
200
Crosstrack Number

Fig. 2. Prelaunch and estimated spectral property of band 41.

-0.01
-0.02
1020 30 40 50
Band Number

Fig. 1. (a)Original band 31, (b) MNF band 1 obtained frombands 8 to 57 of a desert scene in Chile, (c) spectral profile ofthe scene, and (d) weighting factors indicating the contribution to MNF1.
where W and H are the width and height of the image, respectively, and N is the total number of bands. The residualr = s − ŝ is the noise value of each pixel. The coefficient a
is computed so as to minimize Σr2 .
For Hyperion images with significant smile, there is abrightness gradient appearing in the first eigenvalue image,
MNF1. Fig. 1 shows the MNF1 of bands 8 (426.8nm) to 57(925.4nm) for a desert scene in Chile, the spectral profile ofthe scene, and the weighting factors that indicate the contribution to MNF1. In this case, MNF1 is mainly dominated bybands 40 (752.4nm) and 42 (772.8nm), near the oxygen absorption band 41 (762.6nm). This means that bands 40 and 42
are strongly affected by the smile effect because of distorted
oxygen absorption line. Thus, we used the radiance difference between bands 40 and 42 as a spectral misregistration
indicator.
The prelaunch spectral property of the Hyperion VNIR
was characterized by the developer TRW using multispectral test bed [3]. Most researchers use TRW information tocorrect spectral misregistration. However, past research has
shown that the prelaunch spectral property is changed after
the launch [4, 5].
We estimated the spectral property of band 41 of the oxygen absorption line in the x-λ plane using image registration by normalized cross-correlation. The normalized crosscorrelation function between the source and destination subscenes in the line, which is calculated by Eq. (2), is shown at
the bottom of the page. S(i, j) and D(i, j) denote pixel

values of the source and destination subscenes of (i, j). The
size of the correlation window (Lx , Lλ ) is set as (1, 7) for detection of spectral misregistration. Using parabola fitting overthree pixel grid points with their similarity, sub-pixel estimation of spectral misregistration D̂spectral is given byD̂spectral =C(0, −1) − C(0, 1).
2C(0, −1) − 4C(0, 0) + 2C(0, 1)(3)Along crosstrack direction this estimation can detect thedistortion of the line, which shows smaller values because of
pixel-locking effect [6]. Pixel-locking effect is the phenomenon that the estimated positions tend to be biased toward integer values depending on image characteristics, thesimilarity function, and the fitting function. By making a simulation data with applied smile property, we examined pixellocking effect. After correction of pixel-locking effect, thespectral property of band 41 is finally determined. Estimatedproperty is slightly different from the prelaunch property as
shown in Fig. 2.
We have corrected bands 40 and 42 using cubic splineinterpolation with the estimated spectral property and haveevaluated the validity of the correction with the radiance difference between bands 40 and 42 those are much influencedby smile effect. Fig. 3 showed the radiance differences between bands 40 and 42. In the original data, the smile brightness gradient can be seen clearly. In the data corrected using
prelaunch spectral property, there is a little brightness gradient because of overcorrection. In the data corrected using estimated spectral property, there is no brightness gradient. Asa result, the estimated spectral property is proved to be valid
based on MNF analysis and band-difference.
Spectral smile correction of band 41 has failed because
cubic spline interpolation can not restore the original deep absorption spectrum. Thus, absorption bands should be avoided
as a preparation in hyperspectral image processing, such asclassification and band rationing.

∑Lλ ∑Lxji {S(i, j) − S} × {D(i − m, j − n) − D}C(m, n) = ∑Lλ ∑Lx∑Lλ ∑Lx
22 1/2[ ji {S(i, j) − S} ×ji {D(i − m, j − n) − D} ]

(2)

0.2

x=21

x=67

x=96

x=181

(b)

(c)

0-0.1
-0.2

0.2
0.1
0100-0.1

0-0.2-100-200

10-300-500
030

102030

40

50

0.15

Fig. 3. The radiance differences between bands 40 and 42: (a)original spectral misregistration, (b) corrected by prelaunch
spectral property, (c) corrected by estimated spectral property,
and illustration of (d) horizontal profiles of (a)-(c).

OriginalCorrected

0.1
0.05
0-0.05-0.1-0.15

1020304050

Band Number

3. SPATIAL MISREGISTRATION
The prelaunch spatial misregistration was measured at 20locations using a point source [3]. The postlaunch spatial
misregistration was also measured by TRW using lunar images obtained on orbit. However, this information is not
attached to the Hyperion data. Therefore almost all users
use the data without correcting spatial misregistration. Paststudies on spatial misregistration detection proposed a scenebased method using edge detection by sharpening filter [7][8].
In this paper, a scene-based point source detection method isproposed for detection of the postlaunch spatial misregistration.
First, point source subscenes where the radiance is high inmany spectral channels on the homogeneous background aredetected. We estimate the spatial misregistration using image
matching for this spectral direction along the bright spectralline in the x-λ plane as the same way as in the detection of thespectral smile. The size of the correlation window (Lx , Lλ )is set as (7, 1) for detection of spatial misregistration. Band31 (660.9nm) was chosen as the reference because of its central position in Hyperion VNIR spectral dimension in the x-λplane and relatively high SNR. Sub-pixel estimation of spatial
misregistration D̂spatial is given byC(−1, 0) − C(1, 0).
2C(−1, 0) − 4C(0, 0) + 2C(1, 0)

50

Fig. 4. Spatial misregistration for four crosstrack positions.

100200
Crosstrack Number(d)D̂spatial =40

Band Number

OriginalPre-launch
Estimated-400

20Spatial Shift (pixel)

Radiance Difference (DN)(a)

Spatial Shift (pixel)

0.1

(4)

This approach was carried out on the Cuprite site acquired

Fig. 5. Comparison of spatial misregistration estimated using
full scenes.
on March 4, 2002. Similar keystone patterns were obtained invarious crosstrack numbers, as shown in Fig. 4. We assumed
that the spatial misregistration was constant in all crosstracknumbers. Fig. 5 shows the spatial misregistration of theHyperion VNIR has been successfully corrected using cubic
spline interpolation. Fig. 6 presents MNF transformation results for a desert scene in Chile. After correction of both spectral and spatial misregistrations, dry rivers that can be clearlyseen in MNF2 has appeared in MNF1 (compare the portions
indicated by arrows in (a) and (b) in Fig. 6). It indicates thatthe proposed detection and correction method improves Hyperion VNIR image quality.
4. SPECTROMETER MODEL
The change in the alignment of the Hyperion VNIR components at the launch has been considered qualitatively by a simulation of basic spectrometer model. The main specificationof this simulation is as follows: view angle is 3 degrees, grating frequency is 1000 lines/mm, and focal point distance is
100 mm. Spot diagram is obtained by rotating mounting angles of slit, grating, and 2D detector array. Fig. 7 shows
the spot diagrams in the cases where mounting angles of slit,

5. CONCLUSIONS

1 km(a)(b)(c)

Fig. 6. MNF1s of (a) before and (b) after correction of spectral and spatial misregistration, and (c) MNF2 after correction.
λ=500nmλ=550nmλ=650nmNo rotation

Spectral band Position (mm)5φ=-4.3461deg
φ=0degφ=4.3461deg
2D detector array

This paper has proposed a postlaunch estimation method for
hyperspectral sensor characteristics. The image registration
by normalized cross-correlation for characteristic lines in thespectrum image can detect spectral and spatial misregistration
for the Hyperion VNIR. Using oxygen absorption line, spectral misregistration of band 41 is obtained. A scene-based
point source subscene where the radiance is high in many
spectral channels enables the detection of the spatial misregistration for each crosstrack number. Cubic spline interpolation using estimated properties has shown its effectivenessfor correction of the spectral and spatial misregistration andthe validity of the estimated Hyperion VNIR characteristics.
The accuracy of proposed postlaunch estimation of the Hyperion properties is comparable to that of the prelaunch measurements, which enables the precise onboard calibration ofhyperspectral sensors.
6. REFERENCES

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-5Grating

5Slit[2] A. A. Green, M. Berman, P. Switzer, and M. D. Craig, A
transformation for ordering multispectral data in terms of image quality with implications for noise removal, IEEE Trans.
Geosci. Remote Sens., vol. 26, no. 1, pp. 65-74, Jan. 1988.

0-5-5

05-505

Crosstrack Position (mm)Fig. 7. Basic spectrometer simulations in which mounting
angles of slit, grating, and 2D array are rotated in 10 degrees
around light axis.
grating, and 2D array are rotated in 10 degrees around lightaxis.
This simulation and the comparison with the Hyperion
spectral and spatial properties between prelaunch and postlaunch can suggest the possible cause of the misalignment atthe launch. It was impossible to obtain the accurate changeof spatial property because the prelaunch spatial property was
not public. As demonstrated in previous section, the spatial
misregistration of Hyperion VNIR was approximated at a linear function of band number. In the case that the absolute
gradient of this linear function has changed smaller, 2D array may have rotated. In contrast, if it has changed larger,
slit of grating may have rotated. Thus, numerically accurateprelaunch spatial property makes it possible to estimate theorigin of the change of sensor characteristics at the launch.

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