Neither iterative registration, using drainage intersection maps for control, nor cross correlation techniques were satisfactory in registering day and night HCMM imagery. A procedure was developed which registers the image pairs by selecting control points and mapping the night thermal image to the daytime thermal and reflectance images using an affine transformation on a 1300 by 1100 pixel image. The resulting image registration is accurate to better than two pixels (RMS) and does not exhibit the significant misregistration that was noted in the temperature-difference and thermal-inertia products supplied by NASA. The affine transformation was determined using simple matrix arithmetic, a step that can be performed rapidly on a minicomputer

Hummer-Miller, S.

Sawatzky, D. L.

Watson, K.

English

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E82-10210
aR_ to P 79
Registration of Heat Capacity dapping Mission
Day and Night Images
by
Kenneth Watson, Susanne Hummer-Miller, and Don L. Sawatzky
(E82-10210) REGISUiAlIGN Of f EAT (.APACI'11
	 Nd1-3583
MAPPING dISSIuN LAY At,D t.16a: 13AGES
(Ge(,iCg1Cal Survey)	 17 ; rL aUzIlE A01
LSLL 0 D	 U11clas
63/4.3 Cv210
Orl irai p'Za :.'lg a by M&Y X a j1S rchaziod
from EROS Data Center
Sioux raps,. SD 57196
Photogrametric Engineering (in press)
Registration of Heat Capacity Mapping Mission
	
L t	 Lay and Right Images
	
d..	 by
Kenneth Watson, Susanne Hummer -Miller, and Don L. Savatsky
U.S. Geological Survey, Box 25046
Denver Federal Center, Denver, CO 80225
Abstract
Registration of thermal images is complicated by distinctive differences
in the appearance of day and night features needed as control in the registra-
tioi, process.	 These changes are unlike those that occur between Landsat
scenes and pose unique constraints. Experimentation with several potentially
promising techniques has lead to selection of a fairly simple scheme for
registration of data from the experimental thermal satellite HOO-1 using an
affine transformation. Two registration examples are provided.
Introduction
The technique of thermal -inertia mapping (Watson, 1971; Watson and oth-
ers, 1972; Pohn and others, 1974; Watson, 1975; Kahle and others, 1976; Price,
1977; Pratt and Ellyett, 1979; Watson, 1981b) requires co-registering of
images acquired at different times to construct temperature-difference and
thermal-inertia images. In addition, refinements to use topographic correc-
tions (Gillespie and Kahle, 1977; Miller and Watson, 1977; Watson, 1981a) and
	 4
to compare thermal data with other data bases, such as Landsat images and 	
i^
geophysical and geologic maps, require further registration to a topographic
1 -
l
base. Thermal images have some unique characteristics that sake this regis-
tration process more complex than registration of standard .photographs and
Landsat images. The inherent temporal behavior of the surface temperature,
`tlst iepends on albedo, thermal and emissive properties, and slope, elevation,
and moisture effects, makes the scene appear substantially different on day
and night scenes. Texture and topography often are portrayed in an entirely
different manner on thermal images acquired at different times during the day
and night (Sabins, 1969; Rowan and others, 1970; Offield, I975). The selec-
tion of reproducible control points associated with various features thus can
be a difficult task and one subject to substantial error. For example, mislo-
cation of a feature on an in-age with respect to a corresponding topographic
ridge can result in a topographic correction being applied with the wrong
sign! The Heat Capacity Mapping Mission (MCMM) data also presents an addi-
tional problem because the 500-m digital resolution masks many distinctive
features--primarily cultural--that can be seen on the 80-m resolution Landsat
data.
Errors resulting from the misregistration between images and among images
and various data bases have been discussed previously (Miller and Watson,
1977). The purpose of this brief paper is to describe our current experience
with the registration of HCMM data and to suggest the use of a simple method
for registering the data that we have found to be more accurate than the
registered products currently being made available.
2
Method
We experimented with iterative registration, with using drainage-
intersection maps for control, and with cross correlation techniques but none
Mere found to be satisfactory. The procedure finally used to register the
Image pairs was to select control points and to map the night thermal image to
the daytime thermal and reflectance images using an affine transformation on a
1300 by 1100 pixel image. The resulting image reg i stration was accurate to
better than two pixels (rms) and does not exhibit the significant misregistra-
tion that we have noted in the temperature-difference and thermal-inertia
products supplied by NASA.
The affine transformation was determined using simple matrix arithmetic--
a step that can be performed rapidly with matrix hardware on a minicomputer
such as a Hewlett-Packard HP98451-
Consider a set of control points x i ,y i in the day image and the corre-
sponding points x i ',y i ' in the night image. A best-fit affine transformation
can then be computed:
let S' - (xi,yi)
S - (xi,yi,l)
"Use of trade names is for descriptive purposes only and does
not constitute endorsement by the U.S. Geological Survey.
3
then the desired affine transformation
T	 a d
b e
c f
satisfies the matrix equation S' - ST and thus
T - (STS)-1 (STS') where ST is the transpose of S.
Results
The affine transformation provides a rotation correction for the inclined
day and night orbital tracks (see figure 1), an origin shift, and a scale
change. Our previous experience with aircraft data hsr suggested the need to
correct for platform instabilities due to pitch, yaw, and roll of the aircraft
and this has required non-linear or piece-wise linear corrections. We did not
find this necessary with the two HCMM image data sets that we have analyzed.
Presumably the satellite, being more stable, is 1-ss subject to these effects.
The two most important considerations in selecting control features are
that they are readily identifiable on both day and night data and that their
locations cover the scene. We experimented with selecting different types of
features as control points. 	 The best features--those most reproducible on
both day and night data--were the water-dam interfaces of reservoirs. Dis-
tinctive outlines of other water bodies were also reasonably identifiable.
Drainages and topographic features were found to be the least reliable due to
changes in their day-night appearance.	 In the first registration example
(figure 2), nine water body features were used as control.	 In the second
example (figure 3), few water bodies exist, and only three of the six selected
4
control features were water-associated. The other three points are topograph-
ic features. The accuracy of identifying these features was iq part estimated
by the magnitude of the residual errors. A high residual was indicative of
the feature being misidentified. Figure 4 shows an example of the control
vectors (a) and of the residuals (b) from the Powder River Basin scene regis-
tration.
We discovered that a simple transformation was satisfactory over large
parts of an entire image in two relatively arid regions of the western United
States, using a few accurately determined control points at bodies of water.
The procedure should be more easily applied in other areas where bodies of
water are likely to be more abundant. A listing of the computer program to
perform this registration is provided in Appendix 1.
Automatic registration using cross-correlation failed when we attempted
to apply it to one site because of a pronounced topographic grain that pro-
duced a significant error along the direction of the grain.
Some sample results of our registration efforts are provided to illus-
trate the method and its simplicity. Figure 2 shows the transformed night
image compared with the day image and the original untransformed night
image. The second example is the Cabeza Prieta, Arizona, area in figure 3.
Day, night, and thermal-inertia images are presented, the latter being comput-
ed from the registered day thermal, night thermal, and reflectance images
using a new algorithm (Watson, 1981b). Figure 4 presents a comparison between
the original control vectors and their residuals after applying the method to
HCMM data of the Powder River Basin, Wyoming, area.
5
Acknowledgments
This research was supported in part under NASA contract S-110256-B.
6
References
Gillespie, A. R., and Kahle, A. B., 1977, Construction and interpretation of a
digital thermal inertia J.-nage: Photogrammetric Engineering and Remote
Sensing, v. 43, p. 983-i000.
Kahle, A. B., Gillespie, A. R., and Goetz, A. F. H., 1976, Thermal inertia
imaging--A new geologic mapping tool: Geophysical Research Letters 3, p.
26-28.
Miller, S. H., and Watson, Kenneth, 1977, Evaluation of algorithms for geolog-
ical thermal-inertia mapping: Proceedings Ilth International Symposium
on Remote Sensing of Environment, v. 2, p. 1147-1160.
Offield, T. W., 1975, Thermal-infrared images as a basis for structure map-
ping, Front Range and adjacent plains in Colorado: Geological Society of
America Bulletin, v. 86, p. 495-502.
Pohn, H. A., Offield, r. W., and Watson, Kenneth, 1974, Thermal-inertia map-
ping from satellite discrimination of geologic units in Oman: U.S.
Geological Survey Journal of Research, v. 2, no. 2, p. 147-158.
Pratt, D. A., and Ellyett, C. D., 1979, The thermal inertia approach to map-
ping of soil moisture and geology: Remote Sensing Environment, v. 8, p.
151-168.
Price, J. C., 1977, Thermal inertia mapping--A new view of the Earth: Journal
Geophysical Research, v. 82, p. 2582-2590.
Rowan, L. C., Offield, T. W., Watson, Kenneth, Cannon, P. J., and Watson R.
D., 1970, Thermal infrared investigations, Arbuckle Mountains,
Oklahoma: Geological Society of America Bulletin, v. 81, p. 3549 -3562.
7
Sabina, s 1969, "termal infrared imaging and its application to structural
s►appinq, aout!- y California: Geological Society of America Bulletin, v.
80, p. 39i-404.
Witson, Kenneth, 1971, A computer program of thermal modeling for interpreta-
tion of infrared images: National Technical Information Service PB-203-
578.
Watson, Krnnet.h, Po}n, H. A., and Offield, T. W., 1972, Thermal inertia map-
ping Nim`ws satellite data: Eighth International Symposium on Remote
Sensing of Environment (summaries), p. 122.
Watsnn, ht,u. .0t 1975, Geologic applications of thermal infrared images:
Proceedings lrstitute Electrical and Electronic Engineers, v. 63, no. 1,
p. 128-137.
Watson, Kenneth, 1981a, Topographic slope correction for analysis of thermal
infrared images: Submitted to National Technical Information Service.
Watson, Kenneth, 1981b, Regional thermal inertia mapping from an experimental
satellite: Submitted to Geophysics.
a
4
APPENDIX I
ORIGINAL PAGE 19
OF. POOR QUALITY
PFAXO M CEOMX4
C.
C..
Co. :::„. IIEMOTE $MIN* ARRAY PROCES5It1G PROCEDURES
C..... U. S. GEOLOGICAL VAIRVE Y, DENVER, COLORADO
C,,.., BRANCH OF F'ETROFHYfrICS AN8 FCMOTE SENSING
• C...,,
 
DON L. SAWATZKY
C...,.
C
C	 OEOMX6 GENERATES A RECTIFIED IMAGE FILE FROM A DISTORTED IMAGE
C FILE AND FROM COEFFICIENTS FOR RECTIFICATION, R AND S. DETERMINED FOR AN
C AFFINE TRANSFORMATION OF THE DISTORTED FILE.
C INPUT FILE STRUCTURE CONSISTS Of A HEADER RECORD CONTAINING TWO
'C INTEGERS FOR LINE LEWN, LENREC. IU PIXELS AND NUMPER OF LINES.
C NCIRECS. NOJ^ECS NUMI+ER OF DATA RECORDT FOLLOW, EACH RECORD CONTAINING LENREC
C DYIES OF 6-11T DATA. OUTPUT FILE HEADER RECORD CONTAINS TWO INTECERS F LINE
C LENGTH. NPXGUT. AND NUMBER OF RECORDS, LMAX. INPUT PARAMETERS. IPIXIN AND NPI
C )(IN. ALLOW TAKING A SUPSET OF THE INPUT FILE. OUTPUT PARAMETERS LMAX. MNPIX
C . AM MXPIX ARE FELEC ED ON THE LIME LENGTH OF THE INPUT FILE AND DEGREE
C OF ROTATION REOUMED FCA RECTIFICATION. SECTIONS OF THE OUTPUT FILE OF LENGTH
C LMAX AND CONTAINING PIXELS MNPIX TO MXPIX ARE GENERATED BY ONE OR MORE ITER
C ATIONS OF THIS PROGRAM. SECTIONS ARE CONCATENATED IN SUPSEODENT MOC'ESSINO.
C SECTIONING THE OUTPUT FILE IS DONE IN RESKASE TO THE MESSAC+Es "INDUF ARRAY
C TOO SMALL.”
C
C.....DECLARATIV.(S
REAL N(3)9S(3)
LOGICAL*l INbUF(200000).CWT6U€(3000)
INTEGER FC9IN(:35).FC9OUT(?5)
C.....
C.....SET PARAMETERS
WRITE(6999)
99	 FClRMAT(1X.'EkTER PIXEL/LINE COEFFICIENTS ':)
READ(5.96) R.L'
WRITE(6.98)
9e	 FORMAT(1X.'ENTER INPUT FIRST PIXEL, NO. PIXELS')
READ(3.96) IPIXIN.NFIXIN
WRITE(6997)
97	 FORMAT(1X.'ENTER MAX. OUTPUT LINES.MIN/MAX PIXEL '()
READ(5996) LMAX.MNPIX.MXP;X
96	 FORMAT(G16.0)
C.....
Co .... OPEN DATA FILE TO TRANSFORM
READ(S) LENREC . NOREC:S
C.,—SETUP WORK ARRAY
NRECS•MIN(200000/LENREC.NORECS)
MXLINE=NRECS
MNLINE-1
J1•LENREC*MClD(I.NRECS)+i
J2-J1!LENREC-1
DO 90 I-i.NRECS
•0	 READS) (IN8UF (J).J•4ii.J2)
C.....
C,....OPEN OUTPUT DATA FILE
WX0LlTwMXPI l~I X+l
IF(WXOUT.LE.3000.AND.WIXIN.LE.3000)00 TO 110
100 STOP ' {DATA FILES EXCEED BUFFER WIDTH.'
110 WRITE(9) NPXOUT.LMAX
C.-....READ/WRITE LOOP
DO 210 LINEwI.LMAX
DO 200 IPX-MNPIX.MXPIX
9b
ORIGINAL PAGE ;s
Of POOR QVALtTY
INPXnR(i1*(IPX) + R(2101LINE) + PM
IF(INPX.LT.I.OR.INPX.OT.NPIXIN) THEN
OUTKW (1PX-MNPIX+I)!.FALSE.
ELSE
INLINC&S(l)o(IPX) + 91214(LINE) + S(2)
IF(INLINE.4T.1.OR.INLINE.GT .NORECS) THEN
0UT&UF(IPX-MNP1X+i)&.FALSE.
ELSE
CR..... CHECK LIST FOR WANL I NE IN WORK ARRAY
IF(INL1 ►-..C*E.MNLINE.ANL.INLINE.LE.MXLINE) 00TO120
C.....ELSE AEA:- SCANLINE INTO LIST AND W. #RK ARRAY
U :INLIN:.LT.MNLINE) STOP'INDUF ARRAY TOO SMALL!!'
00 IIS I&MXLINE+I.INLINE
IREC•M001I.NRECSISLEMEC
113 REAU(S.REC• INLINE) %INHUF(J).J*IREC•I.IREC+NPXIN)
MXLINEs3NLINE
M'NL INE&MXL INE-NRECS+l
120	 OLIT6+JF(IPX-MNPIX•1)01NDUF(LENREC•MOE,(INLINE.NRECS)•1NPX)
ENDIF
ENDIF
200 CONTINUE
210 WRITE M (OUTPUF(J).JwI.NPXOUT)
C.....
C.....FIN1S
200 STOP
END
2* 4c
List of illustrations
Figure 1.--The day and night orbital tracks of the HCM satellite are inclined
with respect to each other, and the area of overlap is a diamond.
For this example, the scan lines are inclined at an angle of about
300.
Figure 2.--Images of the Powder River Basin area, Wyoming. Figure 2a is the
transformed night thermal image, figure 2b is the day thermal
image, and figure 2c is the original night thermal image. The
scale of the original images is approximately 1:5,000,000.	 The
Bighorn Mountains, Wyoming, are tc the lower left, the Black Hills,
South Dakota, are near the bottom center, and the large body of
water at the top of the scene is Lake Sakakawea, North Dakota.
.5ignificant changes in the appearance of the landscape are evident
between the day and night images. The most apparent aspect of the
offine transformation is a rotation; however, there are also scale
changes in both directions. The usual convention of light associ-
ated with high values and dark with low values was employed.
Extensive cloud cover is present in the upper left hind corner of
the night image and the lower left hand corner of the day image.
10
Figure 3.--Images of the Cabeza Prieta area, southwestern United States and
Mexico. Figure 3a is the day thermal image, figure 3b is the night
thermal image, and figure 3c is a thermal inertia image constructed
by applying a model to registered day-thermal, reflectance, and
night thermal images. The scale of the original image is approxi-
mately 1:5,000,000. The Gulf of California is at the bottom of the
scene and Salton Sea, California is in the upper left corner.
Clouds are present in the upper right hand corner of the night
image.
Figure 4.--Registration vectors between two sets of control points for the
Powder River Basin area, Wyoming. Figure 4a shows the vectors
between the day and night images. Figure 4b shows the residual
vectors after applying the affine transformation. Maximum residual
is 2 pixels.
11
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Registration of Heat Capacity Mapping Mission day and night images

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