The geometric and radiometric characteristics of thematic mapper data through analysis of linear features in the data are investigated. The particular aspects considered are: (1) thematic mapper ground IFOV; (2) radiometric contrast between linear features and background; (3) precision of system geometric correction; (4) band-to-band registration; and (5) potential utility of TM data for linear feature detection especially as compared to MSS data. It is shown that TM data may be used to estimate TM pixel size and to illustrate band-band mis-registration. Further, the geometry and radiometry of the data are sufficiently precise to allow accurate estimation of the widths of linear features. In optimum conditions features one quarter of a pixel in width may be accurately measured. These results have considerable potential for applications for hydrological and topographic mapping

Gurney, C. M.

English

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https://ntrs.nasa.gov/search.jsp?R=19840005562 2020-03-21T01:47:37+00:00Z
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THE USE OF LINEAR FEATURE DETECTION
TO INVESTIGATE THEMATIC MAPPER DATA PERFORMANCE AND PROCESSING
(E84-10037) THE US$ OF LINEAR FEATURE 	 984-13630
DETECTION TO INVESTGGATE ThERATIC RAPPER
DATA PERFORMANCE AND PROCESSING (Systems and
Applied Sciences Corp.) 15 p HC 
A02/Al A 0 1 	 G3/43 00037
Charlotte M. Gurney
Systems and Applied Sciences Corporation
5809 Annapolis Road
Hyattsville, Maryland
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THE USE OF LINEAR FEATURE DETECTION TO INVESTIGATE
THEMATIC MAPPER DATA PERFORMANCE AND PROCESSING
INTRODUCTION
The objective of the work described is to investigate the geometric and radiometric
characteristics of Thematic Mapper data through analysis of linear features in the
data. The particular aspects considered are:
I.	 Thematic Mapper ground IFOV
2.	 Radiometric contrast between linear features and background
S.	 Precision of system geometric correction
4. Band-to-band registration
5. Potential utility of TM data for linear feature detection especially as
compared to MSS data.
MODEL DESCRIPTION
In the context of this investigation linear features are defined as two close, parallel
and opposite edges. Examples in remotely sensed data are features such as roads,
rivers and bridges.
These features may be detected using a local operator which considers the
arrangement of grey levels within a 5 by S pixel array. Details are given in Gurney
(Ref. 1), and the model is described briefly below. Fourteen possible orientations of
the linear feature in the local area are considered, these are illustrated in Fig. I.
For each orientation the sum of values in column B is compared to that in columns A
and C. For dark linear features the sum in B must be less than both the sums in A
and C by a given threshold. In addition B2 must be lower in value than both A2 and
C2. For bright linear features the sums in A and C must be less than those in B. If
these conditions are satisfied for any one of the 14 orientations then pixel B2 is
considered as linear.
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Fig. i Orientation of a Linear
	 Fig.
 2	 Variations in Threshold withFeature Three Pixels Long
	 Linear Feature AlignmentPassing Through the Centre of
an Array
	
with the Pixel Boundaries
Results depend on two factors: the contrast between line material and background
material, and the width of the feature. Any given feature may be assumed to be of
constant width and contrast over the area of study. Therefore by varying the
detector threshold it b possible to vary the accuracy with which the feature is
detected. At low thresholds all of a feature may be detected, as the threshold Is
Increased less and less of the feature will be detected.
An additional factor which must be considered is the alignment of the linear feature
with the pixel boundaries. A simplified schematic representation is illustrated in
Fig. 2. If a feature occupies only a single pixel then a higher threshold may be used
to detect it than if it is equally divided between two pixels. Thus there is a wide
variability between thresholds required to detect the full range of conditions.
If It is assumed that the present material in a pixel is linearly proportional to its
oontribution to the reflectance, and if it is assumed that every alignment of the
feature with the pixel boundaries Is equally probable then it 4 possible to derive a
set of expressions relating detector threshold to detection accuracy, and feature
width and contrast. Complete derivations are given in Gurney (Ref. 2).
_3.
For lines up to i pixel wide:
	
T s 3bW - (3bW - bW/2) A	 (1)
where
T = detector threshold
A = % accuracy / 100
b a contrast z ( I mean background -mean line 1 )/100
W = line width as % pixel width
Similarly for lines i - 2 pixels in width:
	
3b (100 -	 W) 1	 1 + AT= 300b -
	
 _L__T_	 J	 (2)
Similar expressions may be derived for features up to four pixels in width. Beyond
this value detection using this model will not be possible.
Contrast measurements may be made by selecting training area corresponding to
known linear and background materials and using the mean grey level values in these
areas. Since the contrast measurement is based upon mean values it is clear that a
high variance in a training area will lead to results which conform less closely to the
theoretical expectations of the model.
Using this model it is possible to select good thresholds for features of known width
and contrast. Also, by applying the procedure at a given theshold and calculating
observed detection accuracy then it is possible to estimate feature widths, assuming
that the pixel size is known. Ifall parameters are known then it is possible to obtain
an estimate of pixel size. Ali of these applications have been tested using digital
MSS and RBV data with good results.
SCENE CONTRAST AND VARIABILITY
Using MSS data it is generally the case that only one, or maybe two, bands provide
sufficient contrast to allow linear feature detection. With TM data there are more
bands providing good contrast. This allows the possibility of independent verification
from band to band of feature width estimations.
Figs. 3 - 10 illustrate the results of linear feature detection using part of a scene of
Iowa for August 1982. Each of Pip. 3-8 corresponds to detection of bright linear
features in bands 1, 2 9
 30 40 S and 7 using the same threshold (9) in each case.
Variations between results therefore correspond to variations in contrast and scene
variability. Band 6 data, corresponding to the thermal infra-red channel, were not
used since no features were detected.
Results indicate that bands 1 and 3 give the but performance, with band 2 providing
similar results. Bands S and 7 also perform well, but there Is evidence of increased
'noise' i.e., pixels detected as linear which do not correspond to any recognizable
ground feature.
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Fig. S
	 Results of Linear Feature Detection for
Iowa Test Area, Band 1
t r
Fig. 4	 Results of Linear Feature Detection for
Iowa Test Area, Band Z
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Fig. 5
	 Results of Linear Feature Detection for
Iowa Test Area, Band S
Fig. 6	 Results of Linear Feature Detection for
Iowa Test Area, Band 4
—6—
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Fig. 7	 Results of Linear Feature Detection for
Iowa Test Area, Band 5
Fig. 8	 Results of Linear Feature Detection for
Iowa Test Area, Band 7
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o 
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near road at ei fieldnot apparent, and the response see
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Fig. 9 Results of Linear Feature
Detection for Iowa Test Area,
Band 4, Dark Lines
Fig. 10 Results of Linear Feature
Detection for Iowa Test Area,
Band 5, Dark Lines
Figs. 9 and 10 show the results of dark feature detection using bands 4 and 5. Other
bands were unabe to detect dark features successfully. Band 5 performs but in
isolating the rivers without picking up excessive amounts of noise. Band 4 also show
an interesting effect of evenly spaced 'dots' along field edges. The reasons for this
effect are not known at present and will receive further study.
ESTIMATION OF LINEAR FEATURE WIDTHS
Estimation of feature widths using TM data was carried out in order to establish a
lower limit on the width of detectable feature, and to confirm that the data are
suitable for application of the model described previouLly.
Two test areas were selected which had different ground characteristics. The first
corresponds to an area in North Carolina with a very homogeneous flat background
traversed by narrow gravel surfaced roads leas than a pixel in width. The second
corresponds to part of the Washington, D.C. beltway with a width between one and
two pixels and traversing a much more variable background.
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For each test area training areas were selected to establish line: background
contrast. The line detection procedure was then applied at at least three different
thresholds to each band of data for which contrast was sufficient to allow some
degree of feature detection. For each of these the resulting detection accuracy was
then aaleulated. Results were substituted in equations 1 and 2 in order to obtain an
atimate of the feature width as a percent of the pixel width.
Tables i and 2 and Fig. 11 show results for the North Carolina area.
Table 1.
LINE: BACKGROUND CONTRASTS FOR NORTH CAROLINA TEST AREA
-	 Band
Number Background Line Contrast Deviation
X	 o X o
1 64.5	 1.5 107.4 14.9 .43 38.4
- =	 2 24.6
	 0.9 54.6 9.9 .30 36.3
3 21.7	 2.6 69.3 15.6 .48 38.3
4 62.7	 4.0 81.8 10.4 .19 75.4
5 41.8	 6.8 139.6 26.4 .98 33.9
6
7 12.4	 2.8 79.0 21.0 .67 35.8
Contrast = (Imean background - mean line{) /100.
Deviation = (obackground + lino)/contrast.
Table 1 gives the contrast data and an additional parameter termed the deviation.
The deviation is dependent on the amount of variation in the line and background
data, and it essentially indicates the degree to which results are likely to accord with
model predictions. High variances coupled with low contrast leads to considerable
deviation from expected results. From previojs experience using MSS data de'.:--tion
values of 40 or Ion indicate very close correspondence between observed and
predicted results.
-9-
Table 2.
RESULTS FOR NORTH CAROLINA TEST AREA.
Detector Observed Accuracy at
	 Estimated Road Width
Threshold 95% Confidence Level as % Pixel Size
BAND 1 9 81.7-87.5 21.8-25.8
15 55.5-63.3 21.6-24.6
18 35.4-43.2 19.8-21.8
21 25.4-32.6 20.4-22.1
BAND
 9 37.9-45.7 14.6-16.2
12 32.5-40.1 18.3-20.0
15 16.6-23.0 19.3-20.6
' 16 7.1-11.7 21.2-22.2
BAND 3 12 75.0-81.6 22.2-26.0
15 51.6-59.4 18.3-20.6
18 36.8-44.6 18.0-19.9
21 26.1-33.3 18.6-20.2
BAND 4 9 50.8-58.6 27.3-30.9
12 34.4-42.0 29.5-32.4
18 13.0-18.8 35.7-37.4
BAND
 21 69.3-76.3 16.9-19.6
24 63.0-70.4 17.2-19.7
27 57.4-65.2 17.6-20.1
36 45.1-53.1 19.6-22.0
BAND  18 61.1-68.7 18.2-20.9
_ 21 48.4-56.4 17.5-19.7
24
27
42.2-50.2
34.8-42.6
18.4-20.5
18.9-20.8
Road width for
Mean road width (9i6)
	 28.5 pixel
Excluding Band 4 	 20.0	 2.0	 5.7 metres
Including Band 4
	 21.5	 4.7	 6.1 metres
Table 2 lists the observed accuracies and calculated range of road widths for all
band% and thresholds used. Resulis show a close correspondence between estimated
widths at different bands any
 thresholds. with the exception of results for Band 4.
-10-
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Band 4 gives a larger mean width and also shows more variability in the estimates for
different thresholds. This L to be expected from the higher deviation value observed
for tfais band, The estimated road width from all these data is about 6 matres
assuming a 28.5 by 28.5 metre pisel.
a &Now
• 9 . • w w w n Y w w w =P
Fig. it	 Predicted vs. Observed Results for N. Carolina Test Area
Fig. 11 shows a plot of predicted results assuming a 6 metre roadwidth, against
actual observed results. There is a very close correspondence for all but the Band 4
data. The actual width of the roads in this area is not known at the time of writing,
but is believed to be close to the estimate given here.
Table 3.
LINE: BACKGROUND CONTRASTS FOR WASHINGTON, D.C. TEST AREA
Band
Number Background Line Contrast Deviation
X	 o x o
1 56.6	 1.6 73.2 8.5 .17 59.4
2 21.6	 0.8 32.1 5.0 .11 52.7
3 22.8	 1.96 36.6 7.3 .14 66.1
4 35.4	 2.96 43.1 2.4 .07 76.6
5 38.6	 5.7 60.7 10.1 .22 71.8
6 115.1	 P.8 115.7 0.5 .01 130.0
7 13.1	 2.3 28.2 8.4 .15 71.3
Contrast = ( I Mean background - Mean line I )/100.
Deviation = (obackground + o line)/contrast.
-11-
Tables 3 and 4 and Figs. 1 9. and 13 show tesults for the Washington, D.C. a-ea. Band
6 and Band 4 were included in the contrast measurement, but were not used
subsequently because of the poor contrast levels observed. In general, contrasts are
considerably lower than for the North Carolina area reflecting both the difference in
materials and the lower sun angle of this November scene. Devistion values are also
correspondingly higher.
Table 4.
RESULTS FOR WASHINGTON, D.C. TEST AREA
R .
i.
Detector Observed Accuracy % Estimated road width
Threshold at 95% Confidence level as % pixel size
BAND 1 15 83.6-94.6 172.5 -176.9
18 69.9-84.7 170.1 -176.2
21 47.9-65.3 171.2-179.5
24 45.9-63.5 164.8 -172.6
BAND 2 9 65.5-81.2 180.3 -187.8
15 31.9-49.3 173.1-182.1
18 19.5-35.1 167.3-176.1
BAND 3 9 87.4-97.0 179.8 -183.9
15 68.3-83.3 170.1 -176.4
13 58.9-75.5 165.1 -171.9
BAND 5 9 58.0-74.8 198.8 - 209.3
18 32.7-50.1 196.9 - 209.6
27 17.3-32.7 189.1 - 200.8
BAND 7 9 75.2-88.8 184.7 -191.3
15 31.9-49.3 189.3 - 201.1
18 9.1-22.1 198.3-210.0
Road width for
Mean road width (%)	 Q	 28.5 m pixel
Bands 1- 3	 174.96	 5.14
	
49.9 metres
Bands 5, 7	 198.30	 6.63
	 56.5 metres
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Estimated road widths are given in Tab!a 4. These stow a close correspondence
between result ► in bands I t 2, and 3 giving a width estimate of 175% of a pixel which
is close to 50 metres for a 28.5 m pixel. The true width of the beltway is known to
be 48-50 metres in this area so that this result corresponds very closely to the true
width. However results for bands 5 and 7 give a width estimate close to 56 metres
which is an over-estimate of the width.
A. Y Pra&r m Wam"O Nadu Or Was6gam L& Was &,-%	 a. Y MOONS a& Ntir.r Nth Or UmMOMM6 be, Ns Mu
Figs. 12 and 13 Illustrate predicted against 0served results assuming a road with of
50 metres. The correspondence is less close than for the North Carolina data, and is
very poor indeed for bands 5 and 7. The deviation values for these bands are both
very high. However, it is also possible that the Band 7 result is due to a mis-
estimation of the contrast since the slope of the observed line could intercept M, y
axis at the same location as all other bands, s required by the model.
It is concluded that the model developed here may be used to estimate the width of
unknown features in TM data. In optimum conditions with high contrast and
homogeneous materials it appears possible to measure widths as little as one-gw-ter
of a pixel. This has considerable potential for mapping purposes. Contrast levels
observed compare favorably with those for MSS data, although variances and hence
deviation levels tend to be greater. However, the increased dimensionality of the
TM data allows independent verification of results from band to band so that the
results may be considered to be more reliable. Pixel size is confirmed by these
experiments to be 28.5 metres.
HAND: BAND REGISTRATION
Using linear feature detection results it is also possible to investigate band to band
registration. Registration differences wall be apparent as shifts in the locations of
detected features from band to band.
-13-
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Fig. 14 1-95 Interchange, Bands i and
	 Fig. 15
	 1-95 Interchange, Bands 1 and3 Superimposed
	 7 Superimposed
Figs. 14 and 15 illustrate the superposition of results of linear feature detection from
part of the Washington, D.C. scene corresponding to the I-95: Beltway interchange.
Fig. 14 shows bands 1 and 3 together; band 1 results are in black and band 3 in gray.
There is clearly a close correspondence between the two. Fig. 15 shows bands 1 and
7 together. In this case there is a clear mis -registration with the band 7 data being
shifted both to the right and down with respect to the band 1 data. This result was
also observed for band 5 data against band 1. Bands 5 and 7 showed a slight
difference that was diffieuli to assess because of the poor detection rate in band 5.
This method is less suitable for measurement of misregistration than it is for
graphically illustrating that it exists. However, it does appear that there is a shift
between primary and cold focal planes of less than 1 pixel in both directions that is
apparently greater in the x direction than in the y direction.
CONCLUSIONS
It has been shown that TM data may be used to estimate TM pixel size and to
illustrate band: band mis-registration. Further, the geometry and radiometry of the
data are sufficiently precise to allow accurate estimation of the widths of linear
features. In optimum conditions features one quarter of a pixel in width may be
accurately measured. These results have considerable potential for applications for
hydrological and topographic mapping.
—14—
C+
t	 ♦.
ACKNOWLEDGEMENTS
The author would like to thank Mary Aloupis, Margaret Smith and Susan Sanzone for
their help In preparing this paper.
REFERENCES
1. Gurney, C.M., Threshold selection for line detection algorithms, Trans Geosci
and Remote Sensing Vol. GE-18, 2 9 1980 9 pp 204-211
2. Gurney, C.M., The use of contextual information in remote sensi
	 Ph.D.
thesis, University of Reading, 1980.
F -
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The use of linear feature detection to investigate thematic mapper data performance and processing

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