The author has identified the following significant results. The effects of misregistration and the scan-line-straightening algorithm on multispectral data were found to be:  (1) there is greatly increased misregistration in scan-line-straightening data over conic data; (2) scanner caused misregistration between any pairs of channels may not be corrected for in scan-line-straightened data; and (3) this data will have few pure field center pixels than will conic data. A program SIMSIG was developed implementing the signature simulation model. Data processing stages of the experiment were carried out, and an analysis was made of the effects of spatial misregistration on field center classification accuracy. Fifteen signatures originally used for classifying the data were analyzed, showing the following breakdown:  corn (4 signatures), trees (2), brush (1), grasses, weeds, etc. (5), bare soil (1), soybeans (1), and alfalfa (1)

Malila, W. A.

Morgenstern, J. P.

Nalepka, R. F.

English

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nr,	 Developing Processing Techniques for Skylab Data
z	 o	 Monthly Progress Report, May 1975
f"
VTile following report server as the twenty-seventh monthly progress
rn report for EREP Investigation 456 M which it entitled "Developing Pro-0 cessing Techniques for Skylab Data". The iinancial report for this
contract (NAS9-13280) is being submitte' under separate cover.
The purpose of this investigation is to test information extraction
techniques for SKYLAB 5-192 data and compare with results obtained in
applying these techniques to LANDSAT and aircraft scanner data.
Progress on this contract was impeded for much of May by ERIM's
relocation to different facilities. [ERIM has purchased a large laboratory-
office building in Ann Arbor, and the organization is relocating to the now
site.) During the period from May 8 to May 26 the El:lt. computer facility
was being moved and reestablished in the new building and consequently was
clorcd during this period. Work after May 8, therefore, concentrated on
those analyst; tasks that did not require the use of the computer.
During the month work continued on the analyses of the effects of
misregistration in SKYLAB data. Work was also begun o.n the analysis of
the signature set derived from tl;e 5-192 multispectral data for suitability
for use wit'i the subresolution element classifier.
SIGNIFICANT RESULTS:
DETERMINATION OF SPATIAL MISREGISTRATION
We had previously analyzed spatial misregistration effects for conic
(not scan-line-straightened) format data and slowed the existence and extent
of misregistration in it. '11iis month we analyzed the scan-line-straighten.)
,,ata for spatial misregistration. It is a complex topic and the confusion
involved in understand'_nb i.. rises exponentially the deeper one tries to
study it.
Let's begin the explanation in a simple fashion. First, we will defi,e
misregistration as the distance between the -enters of the effective.
iesolved areas in two channels (SDOs) of one pixel.
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101900-57-L
Page 3
By the S-192 system design, all even numbered SDOs are perfectly
registered one with the other; the same is true for all odd  number
SDOs. Further, there is a one-half pixel misregistration betw,7en the
odd numbered SDOs and the even numbered SDOs due to the sampling technique
used. Further misregistration is Introduced by the scanner electronics,
by different response times for different detectors, and/or by improperly
skewed record heads on the spacecraft tape recorder. These combine to
produce the misregistration observed in the conic data. Previous reports
showed that these effects are constant over a single scan line and over
a large set o, scan lines and subsequently could be measured. Also,
remedial effort can be made by shifting SDOs relative to pixel numbers
where the misregistration is close to a multiple. of 1 pixel.
When the scan-line-straightening algorithm rearranges the collected
pixels into scan-line-straightened format, additional spatial misregis-
tration is introduced. The following example gives a graphic account of
th't randomness cf the resulting misregistration and the possible extent
e. it. in what follows we present two pixels each from two conic scan
lines and show the manner in which they are assigned to a straightened
near. line.
1	 IX^	 AV
SG/J nl 
Ar
O ^ ^
Center of odd
numbered SDU
+ Center of ewen
numbered SDO
A and B are the centers
of ecan-line-straightened
pixel (t, odd and even SDOs,
respectively.
To begin the analyses, let us break the 22 SDOs into four subsets and examine
each independently. In what follows we will assume that all SDOs in a sub-
set will be assigned in the same way; this is accurate since the assignment
algorithm is the same for all SDUs and the starting point on a scan line is
the same for all SDOs.
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101900-57-L
Page 4
All ODD numbered, IOW sample rate SDOs f.r)m pixel j in line n will
be assigned to A of scan-line-straightened pixel (SLSP)a. Similarly all
EVEN numbered LOW sample rate SDOs from pixel i, scan line 0 will be
assigned to B. (B being the even numbered SDOs of pixel a.)
All EVEN numbered, HIGH sample rate SDOs from pixel i, scan line n,
will be assigned to A and renamed to be the ODD numbered SDOs of SLSP u.
'	 Similarly all ODD numbered HIGH sample rate SDOs from pixel j scan line 0
(	 will be assigned to B and become the EVEN numbered SDOs for SLSP a.
In each case cited above, the low sample rate and the high sample rate
j	 groups, the misregistration between the even SDOs and the odd SDOs will be
that as found in the conic data -- for the along scan line direction. In
the along track direction there will be on, full pixel misregistration.
Ile misregistration between a set of high sample rate SDOs and a set
of low sample rate SDOs is indeterminate since it depends on whether or
not the even-odd designation for the high sample rate SDOs in the
straightened format has been switched from what it was in the conic format.
Last month we discussed the need to define an inset factor as an aid
in identifying field center pixels for fields whose boundaries are known.
Such an Inset accounts for various errors and effects in the data to insure
that for the field canter pixels identifies,  all SDOs of those pixels are
resolving pure field center areas. We introduced an equation with five com-
ponents to calculate the inset. The contribution to the total inset due to
misregistration effects was referred to as R 
x
, for the component along the
straightened scan line, and R  for the along track component. From examples
such as that cited above, it is possible to calculate R  and 
K  
as follows:
Rx = 1 + M SIN 0	 (pixels)
R  = 1 + M COS 0	 (pixels)
where
M is the maximum misregistration in the conic data
s	 and
0 is the angle between a line drawn tangent to the
conic scan at the scan point of the field of
interest and a line drawn perpendicular to the
straightened scan line.
..CONIC.
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101900-57-1.
Page 5
If the computer program used to implement the insets cannot calculate
a new inset for each point but uses a constant inset, then
R . R - 1 + r^
X	 y
and for this data set, M - .82 so
Rx - R  - 1.82 O xels
would have to be used. Returning to last months discussion we find that
the total inset needed for identifying field center pixels in scan line
straightened data would be:
I = I
x - 
I  - 3.4 pixels
Another observation regarding misregistration in scan line straightened
duca is that it is not possible to correct the data, at least not using a
-i.mple algorithm as was used in the cnnie data. Further, it is not possible even
to correct within any one of the four subsets previously cited, so that misregin-•
tratiun due to scanner electronics could be reduced even within one of
subsets of SDOs.	 That this is the case may be easily shown by using the
figure below.
In the figure pixels A, B, E, and Y will be assigned sequentially to a
stra+.,-%tened scan line. Assume that one SDO, SDO k, is one pixel out of
rcgis::ation with the other SDOs.	 Thus SDO k of pixel B images the area
of pixel A, and S.DO k of pixO. E images the area of pixel 1). Any attempt
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101900-57-1.
rage 6
to simply shift SDO k one pixel relative to all the other SDOs will result
In SUO k of pixel E being the area of pixel B, and not pixel D as would
be correct. It is possible that such a technique would reduce the mis-
registration in some pixels, but it would increase the wisregistration
for other pixels and, more iuportantl y , it would not be possible to know
exactly which pixels were correct and which one- weren't.
Finally, it is obvious that the increased ra sr- gistration caused by
the scan-line-straightening algorithm results in fewer pure field center
pixels and in many more pseudo mixture pixels, i.e., ;,ixels which have
some SDOs imaging field center areas and other SDOs imaging field
boundaries or even completely different fields.
The effects of misregistration and the scan-line-straightening
algorithm on 5-192 data may be stated succinctly:
1. There is greatly increased misregistration in scan-line-
straightened data over conic da,,.
2. Scanner caused misregistration between any pairs of channels
may not be corrected for in scan-line-straightened data.
3. Scan-line-straightened data will have fewer pure field center
pixels than will conic data.
R:rFECTS OF CHANNEL-TO-CHANNEL SPATIAL MISREGISTRATION ON CLASSIFICATION
ACCUKACy AND ON PROPORTION ESTIMATION
Previous reports describe two simulation techniques developed to
investigate the effects of channel-to-channel misregistration on recognition
accuracy. An experiment was also proposed to investigate the effects of
misregistration on field center and border pixel classification. During
this report period a program SIMSIG was developed implementing the signature
simulation modrl; data processing stages of the proposed experiment were
carried out, and an analysis was made of the effects of spatial misregis-
tration on field center classification accuracy. A discussion of this
analysis follows.
Insight was gained into what effects spatial misregistration may have
on field-center recognition performance first through an analytical analysis
of the problem. This analysis examined two normal distributions with common
covariance for any number of channels of data. The conclusions of the
analysis were intriguing. Where 'common sense' might dictate the hypothesis
t
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101900-57-L
Page 7
that misregistration would hurt field-center recognition performance, the
model studied indicated that quite the opposite could '.e true. Under
certain circumstances mir.registration cc •ild actually improve results in
the classification of field center pixels:
Since misregistration and correlation are highly related, the
analysis examined error rate of classification as a function of corre-
lation (1)). It was determined that a unique maximum eircr rate is reached
srmewhere between -1 < P < 1. Figure 1 plots error raze Q, as a function
of correlation P in a conceivable manner as determined by the analysis.
Misregistering data will cause correlation to tend to zero. Therefore,
should the riven correlation 4, between the two stated distributions lie
in the rat-,ge 0 1 m ` perit `. 1 for perfectly registered data, then by
misregistering the data the expected orror rate would actually decrease
in value.
40
FIGW E 1. ERROR RATE OF RECOGNITION I, AS A FUNCTION
OF CORRELATION p IN FIELD CENTERS
In order to test this hypothesis in a real data processing situation,
five Skylab field center signatures representing the distributions of tree,
corn, grass, bare soil and brush classes were selected for use in the imple-
mentation of the experiment proposed last month. Using the algorithm discussed
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101900-57-1.
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in the last monthly report, signatures representing; field center distri-
butions misregistered by factors of 1/3, 1/2, 2/3 and 1 whole pixel in
the SWs 2, 12 and 17 were simulated using the program SIMSIG. 71hese
three SDOs were chosen because they were found to be the three best
channels for purposes of discrimination for the given signnture set.
An expected performance matrix was calculated for each of the four sets
of simulated signatures .long with the original signature set using; the
program PEC mentioned in the April mccnthly. Table 1 difplays several of
the calculations
TABLE 1. EXPECTED PERFORMANCE OF SWY LAB SIGNATURES
FOR VARYING DEGREES OF MISKEGISTRATION
Degree of
Misregistration Expected Recognition Accuracy
Tree Grass	 Bare Brush Corn Total
0 pixels 96.5 81.3	 97.9 77.2 77.9 86.16
1/3 96.3 80.3	 98.2 76.8 78.2 85.96
1/2 96.1 81.1	 :8.1 76.0 78.4 85.94
2/, 96.2 81.8	 98.1 76.0 79.0 86.22
1 96.7 82.8	 98.7 76.4 78.4 86.60
The results displayed in this table seem to support the unexpected
hypothesis that misregistration need not be harmful to the recognition
performance of field center pixels. Note that in the total expected
classifJcati4m for the given signat , ire set, for nisregistration in three
channels of 112 a pixel the performance diminishes slightly but as mare
misregistration is introduced, the : ►erformance improves somewhat.
These results, however, ch*ui.d not suggest using misregistered data
er actually misregistering data to improve recognition. This experiment
indicates that while spatial misregistration may aid in the classification
of pure field center pixels, two serious problems arise: (1) misregistering
data f,as the effect of greatly dEcreasing the number of pure field center
pixels; and (2) the classification of mixture pixels will be Advorsely
affec < <d especially causing a greater number of false alarms. Thy second
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101900-57-L
Page 9
effect is still in the hypothesis; stage. The analysis of this coming
Month will test this, centering oa the effects of spatial misregistration
of border pixels. To date, data has been processed in the manner proposed
in the lash monthly. One parameter that had been fixed was the number of
channels to be misregistered. The three best channels have been misregistered
In simulation and further processing will be carried out in which only the
one best channel will be misregistered. This additional information will
enable us to examine to a degree the effects of channel-to-channel mis-
registration as a function of the number of channels out of registration.
GEOMETRIC ANALYSIS OF TILE SIGNATI)RE SET
In order to use the subresnlution element classifier, or mixtures;
classifier as we tend to call it, it is necessary that the signatures being
used are sufficiently distant in n-space one from the- others, otherwise the
algorithm breaks aown. It is alao necessary to minimize the number of
signatures used for classifying in order to minimize costs. The first
order of business then was to identify a small subset of signatures
and then to analyze them for separation.
An analysis of the 15 signatureb originally used for classifying the
data showed the following breakdown:
CORN	 4 Signatures
TREES	 2 Signatures
BRUSH	 1 Signature
GRASSES, WEEDS, ETC.
	
5 Signatures
BARE SOIL	 1 Signature
SOYBEANS	 1 Signriture
ALFALFA	 1 Signature
Since soybeans and alfalfa are very minor ground covers in the test
site, we excluded them from this study. An analysis of the tree and brush
signatures showed the two tree siinatures to be very disparate, but that
the brush and one of the tree signatures were very similar spectrally --
overlapping some 75%. The brush signature, representing primarily areas
of scrub forest, was therefore combined with the one tree signature. As
for the corn signatures, the t ,^o signattres with most of the corn points
were found to be very different; since corn is a major cover, boti, these
signatures were selected for use. The bare soil signature was inc Wded.
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101900-57-1,
Page 10
For the grasses, rather than combining; signatures, resulting in a signa-
ture with a very large spread. we endeavored to choose just one signature.
From ::n examination of 2-dimensional scatter plots of all the signatures
we identified one &r:ss signature which was always more toward tale vege-
tative vertex of the so called cluster pattern. We selected this grast,
signature reasoning that pixels from pasture or weed fields would most
likely be called a mixture of the gra-,s and bare soil.
The signature set thus identified was used as an input to program
G£JM which calculates measures of separateness for a signature simplex.
'llic metric -alculated if; roughly the distance between the mean of one
signaturf: an! the hvperplane through the other signatures expressed in
standard deviations. If the metric calculated for a given signature it,
small., then the simplex is too degenerate and the mixtures algorithm will
not work well.
the table below, Table 2, lists the GEOM output for each pair of
signatures from the set. We consider this output because in the current
Implementation of the mixture classifier we will find the most likely
mixture of two classes for each pixel. In general, a GLOM number greater
tljan 2.5 is considered to indicate a diverse enough si-gnature set to allow
the running of the mixtures classifier. For the most part the numbers in
the table are well above this level. This is taken to be an encouraging
result.
During; the coming month we will continue our analysis of the signa-
tures and their implementation on the mixtures classifier.
TABLE 2. GEOM OUTPUTS FOR 6 SIGNATURE SET
(Read RESULT for Row Signature to Hyperplane of Column Entry)
CORN 1 CORN 4 GRASS 2 BARE SOIL SCRUB FOREST TREE 3
CORN 1 - 2.8 4.6 6.0 2.9 3.8
CORN 4 3.6 - 6.5 11.6 1.9 4.2
GRASS 2 8.2 7.5 - 16.6 7.7 15.6
BARE SOIL, 8.2 9.8 9.0 - 8.9 8.0
SCRUB FOREST 2.0 1.6 3.9 6.7 - 3.6
TREES 7.4 10.3 14.9 10.0 9.6 -
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101900-57-1.
Page 11
Submitted by:^ , 'Z,.1	 r	 lip4
Richard N. Nalepka^
Principal Investigator
Approved by:
J i D. Erickson
Nerd, Information Systems
and Analysis Department
Approved by:	 o-'--x 'J^	 C,
Richard R. Legault
Director, Infrared and
Optics Division
1
dd
i


Developing processing techniques for Skylab data

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