The author has identified the following significant results. Automatic stratification of forested land from ERTS-1 data provides a valuable tool for resource management. The results are useful for wood product yield estimates, recreation and wildlife management, forest inventory, and forest condition monitoring. Automatic procedures based on both multispectral and spatial features are evaluated. With five classes, training and testing on the same samples, classification accuracy of 74 percent was achieved using the MSS multispectral features. When adding texture computed from 8 x 8 arrays, classification accuracy of 90 percent was obtained

Johnson, G. R.

Kirvida, L.

Swanlund, G. D.

English

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20 March 1973
TYPE II
PROGRESS REPORT
ON
AUTOMATIC PHOTOINTERPRETATION FOR PLANT SPECIES
AND STRESS IDENTIFICATION (ERTS-A1)
ERTS
Proposal Number MMC 647
Principal Investigator Number PR 202
Contract Number NAS5-2186
Prepared by: L. Kirvida Approved
G. R. Johnson G.D. Swanlund
Honeywell, Inc.
Systems and Research Division
2345 Walnut Street
St. Paul, Minnesota 55113
Original photography may be purchased froml
EROS Data Center
IO0h and Dakota Avenue
Sioux ralls, SD 57198
https://ntrs.nasa.gov/search.jsp?R=19730010634 2020-03-23T03:32:16+00:00Z
AUTOMATIC INTERPRETATION OF ERTS DATA
FOR FOREST MANAGEMENT
Leonar4 l Krvda and G1reg ;r Tohns~o
Systems & Research Div., Honeywell Inc. and
College of Forestry, Univ. of Minn.
St. Paul, Minn.
ABSTRACT
Automatic stratification of forested land
from ERTS-A data provides a valuable tool for re-
source management. The results are useful for
wood product yield estimates, recreation and wild
life management, forest inventory and forest con-
dition monitoring. Automatic procedures based on
both multi-spectral and spatial features are evalu-
ated. With five classes, training and testing on the
same samples, classification accuracy of 74% was
achieved using the MSS multi-spectral features.
When adding texture computed fromm8 x 8 arrays,
classification accuracy of 99% was obtained.
INTRODUCTION
Managers of forest resources are faced with increasing de-
mands for forest products as well as the needs of alternative uses
for the land surface. Consideration must be given to the environ-
mental problems associated with the' removal of forest by harvesting,
diseases or pests, and the effects of forest areas on the ever-
increasing needs far pure water.
ERTS-A data have opened up many possibilities for effective
management. However, new processing and analysis techniques
will be required to exploit these data. In particular, automatic data
processing appears mandatory for many interpretation and inventory
functions. For example, automatic stratification by type and den-
sity class provides a common basis for multiple uses. The data are
formatted for convenient insertion into a computerized data bank.
Processing of repetitive coverages increases the accuracy of the
inventory data and detects changes and trends.
Our investigation is to determine both the feasibility of auto-
matic interpretation from ERTS-A data and the insertion of the data
into a computerized data bank. This is a cooperative effort with the
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University of Minnesota and the Minnesota State Planning Agency.
Initial results on the automatic classification of areas by forest type
are described herein.
PROCEDURE
The automatic classification of forested areas to be discussed
was performed for the Cloquet, Minnesota area, located 25 miles
west of Duluth. An aerial photograph of the approximately 24, 000
acres of forest and associated types:that were stratified is shown in
the left half of Figure 1. Ground truth information was obtained by
using aerial photo interpreters from the University of Minnesota's
Institute of Agriculture Remote Sensing Laboratory (IARSL) located
in the College of Forestry. Since the College's Cloquet Forestry
-Center, an experimental forest, is in the midst of this area, much
'information was previously known about the forest types. Spring
1:90, 000 panchromatic aerial photographs,numerous field checks,
and previous ground experience in the study area were used by the
interpreters in generating the ground truth map. The Cloquet area
was delineated into five types: conifers, hardwoods, open, water,
and city.
.: 
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-. - ..
E .: .,: . ,~. - ;. .I , ~ --
|' :' ..- ' : ' : ' '. ' ' -- ' -.
AERIAL ERTS - Band 7
Figure 1. Aerial and ERTS Images of Cloquet
Study Area
The features used for classification were derived from the
four MSS bands of ERTS-A image 1075-16312, an October 6, 1972
cloud free coverage of the Cioquet study area. Data from the bulk,
black and white 70 mm transparencies and 7 track 800 BPI computer
compatible tapes (CCT) were used as the data base. The imagery
-2-
was used for orientation and registration, and the digital data was
used to perform the automatic stratification and analysis.
The ERTS digital data of the study area was then reproduced
on film by writing with a digital magnetic tape to film printer for
purposes of registering with ground truth information. The film out-
put for Band 7 is shown on the right half of Figure 1. It provides an
image of the study area containing grid lines corresponding to record
and word on the digital magnetic data tape. Registration of ground
'truth with ERTS-A data was accomplished by recognition of landmarks
such as water bodies in the area. Registration with ground truth
maps was required for both training and evaluating the automatic
classification system and for producing the stratification output.
Once ground truth and ERTS-A data were registered, type
boundaries were encoded in terms of record and word numbers.
From within the type boundaries, 8 x 8 arrays were isolated to
serve as training samples. During training two categories of fea-
tures, multi-spectral and texture, were generated for a number of
8 x 8 array samples in each class as illustrated in Figure 2.
- . : .. .·
DENSITY FEATURES- .8 .8 FOUR BANDS --- -- - * i
RAW DATA 1, 2 3 4 
SAMPLES MOMENTS OF EACH - ;
' ~~ .- -2, 3, 4'
[WALSH _ .
...... COEFFICIENTSC· I COEFFICIENTS ~~a) DENSITY ONLY
25/BAND (4 BANDS) REDUCE NUrMBER OF
b) DENSITY (4.BANDS) HARMONICS TO SIMU-
TEXTURE TRANSFORM LATE EFFECT OF RE-
(BAND 41 DUCTION IN SAMPLE
c) MOMENTS + WALSH AREA, i.e., 8 x 8, 4 x 4,
U H SLANT TRANSFORM 2 x 2 ARRAYS
TRANSFORM (BAND 4)
25/BAND
FEATURE SET
COOLEY TUKEY TO
TRANSFORM CLASSIFIER
25/BAND
Figure 2. ERTS-A Feature Extraction Procedure
-3-
The first experiment was. run using. only, spectral features.
An illustration of the separability between the five classes is shown
by the density histograms in Figure 3. The four sets of histograms
were derived from each of the four IMSS bands. Band 4 has a great
deal of overlap between classes. Band 7 is excellent for separating
water from land and was usually used for locating lakes for ground
truth landmarks.
Two dimensional histograms for the five classes were also
computed. These indicated high cross correlation between some pairs
of bands for a few classes; however, no consistent conclusions could
b: e drawn. . :
BAND4 SAND S 
- 40 J CONIFER WATER
OPEN GROUND 40 CONIFER I
: -30- - WATER_ l ,30- -\I
- - ;20- -U| HARDWOOD OPEN
: - :.-10- - ITY o *J HAPDWOOD
I0~~~~~ -
0 8 16 24 32 40 0 8- i6 24 a 40.
- * .INTENSITY - INTENSITY i
BAND 6 BAND 7
* 50- 50
WATER
40 -:40-
- -.4 WATER HARDI1OOD 40 CONIFER
Z 30- \-\ Z. 30I: ': - HARDWOOD -O
-: . ,20- - r Aft \ / i=, 20-. O,
10 O+~OPEN CITY- lo- | \- X PE l- 0  I C ITY X
0 8 16 24 32 40 8 16 24 32 40
INTENSITY . INTENSITY
Figure 3. SHistograms of Intensity Levels
ERTS 1075-16312
For texture features, two dimensional Fast Walsh, Fast
Fourier and Slant Transforms were utilized. Texture features were
computed from an 8 x 8 array representing approximately 70 ground
acres. Texture was also computed on a 4 x 4 array to determine the
effect of array size on performance. Increasing the array size in-
creases frequency resolution which increases the classification
-4-
accuracy. However, the larger array size increases the minimum
area to be classified.
Waving selected thie featres t be Used andithe training set
a Linear discrilifaklt classifier is trained. Briefly, the classifier
algorithm groups each of the features of. the training set around an
orthogonal basis vector in a least mean square sense. The "weight"
matrix required to do this is computed for subsequent application to
the input data during testing and during the generation of overlay
maps. The class to which the input data point belongs is determined
by the distance from the various orthogonal vectorpoints. A block
diagram illustrating the procedures used for automatic interpretation
is shown in Figure 4.
CCT (BULK)
AIRCRAFT OVERFLIGHT
PHOTOS AND STRATIFI-
CATION MAPS CONTAIN- 
ING LANDMARKS . RE-FORMAT
BULK ERTS ' REGISTRATION ; FILM
PHOTOS . ERROR DETECT WRITER
REGISTER
" "' - CC. (BULK ."
GROUND TRUTH TO B AIPHOTO I I~lsou~on~v I ,I SAMPLE
. . · . A INTERPRETATION ENCODER FI EXTRACTOR 
COLOR |AP DENSITYA_.
.COMBINGER I ISLICER L 1 ~--~9 1FEATURE EXTRACTOR
PATTERN
RECOGNITION R IULTI-
TRAINER TEXTURSPECTRALI
'~~~~~~~~~~~~~~~~~~~~L _
Figure 4. Data Analysis Procedures
-5-
: 
- I
RESULTS
USing only multi-spectral features., Rthe classifier was
·eetiled oil data derived -fro m the X 8 sampTe arrays frore, each
class. The total training area was 4687 acres proportioned into
five classes. Each training sample consisted of one data point, ap-
proximately one acre. After training, a delineation map was gen-
erated for the 24, 000 acres including the training area. The results
of the automatic classification are shown in the top half of Figure 5.
Decisions were made for each acre data sample. The five classes,
hardwoods, conifers, open, water, and urban, are presented as
density levels increasing in density in the order listed. The per-
formance of the automatic classification procedure can be estimated
by comparing its output on the top half of Figure 5 with the photo
interpreter overlay on the bottom half of Figure 5. The distortion
of the classification output photo is due to the unequal spacing be-
tween ERTS samples in the x and y directions.
.Z4 ' ~ ". ',' Y.":' '~'' " '.' ". _.:>, 'Z' ~: J '
rave- ... Am..
I+ , ~ ~~<' ~LJ~-...Y,. . ....... , , L , -..-- :- . ,- o,
so.~ e :, '..:.-..': ::: '..~..:... Y '. -. ; .
·~~~~~~~~~~~~~~~~~~~~~~~~~~~i
I J V _ Jo -- 5 - -. -. - v w 
:x; his I
A_...A. . ..... 3 ..._:.M._-__ A .... ,
*.s. ":,.-... _. - ,~aft,
'~~~~~~~ L---L;.J·?.W~.R..K"
Figure 5.
Key
-H - Hardwood
C - Conifer
O - Open
W - Water
T - City
Automatic Classifier Results Based
on Density and Ground Truth
When the classifier is trained and tested on the same data
points, its performance is shown by the confusion matrix in Table I.
Seventy-four percent of the data points are correctly classified. An
indication of the types of mistakes made are shown in the confusion
matrix. Notice that confusion is most common where city is called
open, the next most common is open classed as hardwood and then
open classed as city.
-6-
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Table I. Automatic Classification Results Based on Density
Sample Automatic Classification Assignments in Percent
Tvne Size Hardwroorrd rnrifer Onon T-.h7fr C; e-.
Hardwood 896 80.7 4.1 4.9 1.8
Conifer 1152 .5 80.9 2.3 13.9 2.4
Open 960 20.4 3.5 61.6 .6 13.9
Water 640 10.2 .0 .3 88.6 .9
City 640 8.1 6.1 24.1- 6.4 55.3
Total 4288 Correct Classification Assignments = 3167
The performance using only multi-spectral data is shown also in
Figure 6. When training and testing on individual data points, the
performance is 74%7using the 4 MSS density bands as-features.
An indication of the effect of adding texture to multi-spectral
features .is shown by the curve in Figure 6. Texture was computed
from Band 7 using the Slant Transform. The Slant Transform is an
-image transform with a basis vector matched to the gradual bright-
ness changes along an image line which compacts the image energy
to as few of the transform domain samples as possible. It can be
computed using a fast computational algorithm. When computed on
a 4 x 4 array,.the performance increases to 90% and if computed on
. an 8 x 8 array, the performance increases to 99%.
Z 100z lOo
U
a.-
U
8 0
. SLANTX + DENSITY (8 x 8)
SLANTX + DENSITY
(4 x 4)
WALSH + MOMENTS BAND 7
o
. FAST FOURIER BAND 7 . .
DENSITY
,
4
! I I
a 12 16
NUMBER OF FEATURES
24 8
20 24 28
Figure 6. Performance Using Both Density and Texture
Texture was also computed using the Cooley-Tukey Fast Fourier
and Walsh Transforms. The performance of these two transforms is
generally very similar, the Walsh Transform utilizing a binary wave-
form for computational advantages. When an 3 x 8 array from Band 7
17
8.5
I
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.
was used for calculating the Walsh coefficients and the first through
the fourth moments of density from Band 7 were used for density fea-
tures, the performance classification accuracy was 91%. This is an
nf8catiao of the performance achievable using only Pend 7 for inputda.a.
The final point on Figure 6 was computed using the Cooley-Tukey
Fast Fourier Transform on an 8 x 8 array from Band 7. Only the first
four spectral components were used resulting in 16 features. The low-
est frequency component contains the least amount of information and
contributes 9 of the features. In the interest of minimizing 'features,
it was deleted and a resulting performance of 89% was achieved..
DISCUSSION
Even though the results given in this paper are of a preliminary
nature derived from a small test site, they indicate the feasibility
- --~·· of automatic stratification. In further study they will be extended
- in the following ways:(l) increased area of coverage to provide a
larger statistical base, (2) inclusion of a finer classification by
forest cover type., site, and stand density, (3) incorporation of data
from multiple coverage, (4) comparison with other texture measurement
techniques, and (5) application to a national forest already under an
intensified management system.
The information content of the texture features may be improved
By applying the Fast Fourier Transform to two ISS bands simultaneously
-with one-band as the real input and the second as the imaginary input.
The easily calculated Haar Transform may be adequate for texture, but
its performance should be compared with the optimum performance ach-
ievable with the Karhunen-Lowe Transform.- The latter transform is
difficult to compute, but it provides the least mean square truncation
error and the maximum entropy interpretation. In addition, performance
of the linear classifier may be improved by cluster analysis whereby
classes are generated on the basis of similarity of features.
Stratification information is useful to natural resource land
managers. Our goal is to determine the capabilities of automatic
classification from ERTS-A data, the maximum number of classes, and
an acceptable operational data format. In addition, we seek to deter-
mine the best combination of automatic and human interpretation. We
will compare automatic techniques to studies being done by IARSL on
the Chippewa National Forest and the state of Minnesota Land Manage-
ment Information System.
4
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Automatic photointerpretation for plant species and stress identification (ERTS-A1)

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