Significant progress has been made in the classification of surface conditions (land uses) with computer-implemented techniques based on the use of ERTS digital data and pattern recognition software. The supervised technique presently used at the NASA Earth Resources Laboratory is based on maximum likelihood ratioing with a digital table look-up approach to classification. After classification, colors are assigned to the various surface conditions (land uses) classified, and the color-coded classification is film recorded on either positive or negative 9 1/2 in. film at the scale desired. Prints of the film strips are then mosaicked and photographed to produce a land use map in the format desired. Computer extraction of statistical information is performed to show the extent of each surface condition (land use) within any given land unit that can be identified in the image. Evaluations of the product indicate that classification accuracy is well within the limits for use by land resource managers and administrators. Classifications performed with digital data acquired during different seasons indicate that the combination of two or more classifications offer even better accuracy

Joyce, A. T.

English

NASA Technical Reports Server

Paper L 1 
COMPUTER-IMPLEMENTED LAND USE CLASSIFICATION WITH PATTERN 
RECOGNITION SOFTWARE AND ERTS DIGITAL DATA 
Arrnond T. Joyce, NASA Earth Resources Leboretory, Mississippi Test Facility, Bay St. Louis, 
MS 39520 and Thomas W. Pendleton, lbid 
ABSTRACT 
ssigned to the various surface COI 
r-coded classification is film re( 
film at the scale desired. Print: - -  
Significant progress has been made in the classification of surface conditions 
(land uses) with computer-implemented techniques based on the use of ERTS digital 
data and pattern recognition software. 
at the NASA Earth Resources Laboratory is based on maximum likelihood ratioing 
with a digital table look-up approach to classification. 
colors are a iditions (land uses) classified, 
and the colo :orded on either positive or  nega- 
tive 9 1/2" 
mosaicked and photographed to produce a land use map in the format desired. 
puter extraction of statistical information is performed to show the extent of 
each surface condition (land use) within any given land unit (e.g. township, 
county, drainage, etc.) that can be identified in the image. Evaluations of 
the product indicate that classification accuracy is well within the limits for 
use by land resource managers and administrators. Classifications performed 
with digital data acquired during different seasons indicate that the combina- 
tion of two o r  more classifications offer even better accuracy. 
will include adaptation of software to general purpose computers, development 
of low-cos1 establishment of ground truth logi- 
stics for 1 'ge areas, e.g. statewide. 
The supervised technique presently used 
After classification, 
; of the film strips are then 
Com- 
Future emphasis 
INTRODUCTI( 
: hardware for image display,-and 
ddespread implementation over lax 
ERTS-1 data offers the land use analyst several new dimensions. A single 
ERTS pass results in the collection of data over a swath approximately 100 
nautical miles wide, whereas imagery acquired with mapping cameras flown in air- 
craft commonly cover swaths from two to fifteen nautical miles. ERTS repetitive 
coverage on an eighteen day cycle provides possibilities for a rapid detection 
of cultural changes on the earth's surface as well as seasonal differences in 
vegetation and land use practices. 
conducive to automated data processing based on computerized systems. 
In addition, the digital form of the data is 
The objective of the study reported in this paper was to perform computer- 
implemented land use classifications utilizing ERTS digital data and pattern 
recognition software for two sets of data, each pertaining to a different season 
of the year, and to compare the two classifications as to their portrayal of 
seasonal differences in vegetation and agricultural practices. 
acquired over the Mississippi coastal plains on August 5, 1972, and again on 
January 16, 1973 were selected for the study. 
ERTS digital data 
*Nasa Earth Resources Laboratory, Mississippi Test Facility, Bay St. Louis, MS 
39520 
**Ibid.. 
331 
https://ntrs.nasa.gov/search.jsp?R=19740022616 2020-03-17T15:20:00+00:00Z
DATA PROCESSING 
Land use c l a s s i f i c a t i o n  a t  t he  Earth Resources Laboratory i s  performed using 
a Data Analysis S t a t ion  (DAS) and UNIVAC 1108 software. The DAS cons i s t s  of a 
Varian 620f computer with 16,000 16 b i t  words, two nine t r ack  d i g i t a l  tape decks, 
a color t e l ev i s ion  display device (CRT) with l i g h t  pen capab i l i t y ,  a Singer color  
f i lm recorder,  a card reader and a l i n e  p r i n t e r .  The UNIVAC 1108 software con- 
sists of s eve ra l  modules which c o n s t i t u t e  a supervised maximum l ikel ihood classi- 
f i c a t i o n  scheme based on Gaussian statist ics.  The modules are a s t a t i s t i c a l  
module, a t r a in ing  sample separat ion module,and a c l a s s i f i c a t i o n  module. 
The i n i t i a l  s t age  of d a t a  processing cons i s t s  of reformatting on t h e  DAS the  
nine t r a c k  ERTS computer compatible bulk data tapes  received from t h e  Goddard 
Space F l i g h t  Center. 
s u i t a b l e  f o r  t he  1108 software and a display t ape  which can be used t o  d r ive  t h e  
DAS CRT o r  t he  DAS f i lm  recorder. 
capabi l i ty  of t h e  DAS CRT, t he  coordinates of surface areas with known land use, 
called t r a in ing  samples, are determined. 
coordinates allow t h e  t r a i n i n g  sample areas t o  be located i n  t h e  da t a  i n  a super- 
vised c l a s s i f i c a t i o n  system. 
formatted bulk data  tapes,  t h e  t r a in ing  sample da t a  i s  extracted and s tored on a 
t r a in ing  sample e d i t  tape which can be used with t h e  UNIVAC 1108 software. 
The reformatting operation produces a da ta  t ape  i n  a format 
Using t h e  display tapes  and t h e  l i g h t  pen 
These scan l i n e  and scan l i n e  element 
Using the  t r a in ing  sample coordinates and t h e  re- 
f ~* 
-G The statist ical  module on the  UNIVAC 1108 i s  used t o  compute means and co- 
variance matrices and t o  p l o t  histograms f o r  each t r a i n i n g  sample. 
t i o n  output by t h e  s ta t i s t ica l  module i s  used t o  e d i t  the t r a i n i n g  sample d a t a  
and is used €or input  t o  t h e  separat ion module. 
a measure, "divergence," of t he  s i m i l a r i t y  of p a i r s  of t r a i n i n g  samples. 
measure,  while quan t i t a t ive ,  i s  d i f f i c u l t  t o  relate t o  physical  processes. 
However, it is  known t h a t  t he  l a r g e r  t h e  measure the  g rea t e r  t h e  d i f f e rence  
between the  t r a i n i n g  samples.  
samples can be grouped to  form a t r a i n i n g  class and which t r a i n i n g  samples can- 
not b e  grouped but must be t r ea t ed  as subclasses.  
f i c a t i o n  of t he  two subject  ERTS frames, t he  divergences between a l l  t r a i n i n g  
The informa- 
- The separat ion module computes 
The 
,' ? 
ERL uses t h e  measure t o  determine which t r a i n i n g  
I n  p a r t i c u l a r ,  f o r  t h e  classi- 
,; samples which p o t e n t i a l l y  belonged t o  a s i n g l e  class were computed. Those train- 
' . -  ing samples which had a divergence of less than approximately 15 w e r e  grouped i n t o  
a s i n g l e  subclass.  
A s  an example of t r a in ing  sample grouping w e  could consider t he  class "forest"  
from t h e  7 August 1972 da ta  set as shown i n  Figure 1. The t r a i n i n g  information 
f o r  the "forest"  c l a s s  consis ted of f i f t e e n  t r a i n i n g  samples i d e n t i f i e d  as p ine  
and twelve samples i d e n t i f i e d  as hardwood. 
t ra ining samples i n t o  th ree  subclasses of pine and four  subclasses of hardwood. 
Hence, t h e  f o r e s t  c l a s s i f i c a t i o n  w a s  derived from seven f o r e s t  subclasses .  
general, t h e  s i x  class c l a s s i f i c a t i o n  w a s  derived from twenty-three subclasses  
which w e r e  t h ree  soybean subclasses,  one corn subclass,  two exposed s o i l  sub- 
classes, two grass  subclasses,  one pas tu re  subclass,  t h r e e  marsh subclasses ,  
three water subclasses,  one urban i n d u s t r i a l  subclass  and t h e  previously mentioned 
seven f o r e s t  subclasses. 
The divergence criteria grouped these  
In 
332 

Based on the groupings indicated in the previous paragraph, the statistical 
The classification algorithm is 
This technique eliminates the need to 
module was used to generate information used by the classification module to 
classify the reformatted ERTS bulk data tapes. 
based on pre-storing in the computer a representation of each data element and 
the class to which it is to be assigned. 
compute 
belongs to each subclass, and the comparison of all such probabilities. 
classification algorithm can process one ERTS cmputer compatible tape in eight 
minutes. However, the algorithm is limited to twelve classes. Since we used 
twenty-three classes, two passes were required per data tape. 
tapes or one ERTS frame requires about one hour to process. 
classification is stored on tape as a color-coded classification symbol for each 
data element. 
for re-occurring data elements the probability that the data element 
The 
Therefore, four 
The resulting 
The classification tape is displayed in false color on the DAS CRT and is 
displayed on the DAS film recorder. 
film recorder, rectification allows overlaying the classification data with a 
map of desired scale. The rectification technique considers scan angle, scan 
rate, sample rate, V/H ratio of the platform, rotation rate of the earth, and 
the characteristics of the film recorder. 
rectification has not yet been made, but rectified data has been overlayed with 
a 1:250,000 scale map on a Traverse Mercator Projection. The match between the 
rectified data and the map appears to be very good in a region 25 x 100 nautical 
miles which corresponds to 1/4 of an ERTS frame and it is expeeted that the 
entire ERTS frame will match equally as well. 
When the classification is displayed on the 
A quantitative evaluation of the 
DATA ANALYSIS 
The classifications for both sets of data were performed using the same 
geographic locations for training sample areas. 
sample areas which together encompassed 4376 resolution cells. 
the’classifications within training sample areas are shown in Table 1. 
There were eighty-two training 
The results of 
Overall, the classification within training sample areas indicates that the 
August 1972 data yielded a more accurate classification than the January 1973 
data (94.8% versus 89.0%), but both classifications are well within the limits 
for use by land resource managers, administrators, or planners. 
evaluation is still in progress, but preliminary findings show that classifica- 
tion accuracy within the entire test area (six counties) is not  substantially 
different from the results of the classification within training sample areas 
However, in viewing the statistics in Table 1, it is evident that certain surface 
conditions were classified more accurately with one set of data than with the other. 
The ground 
The forested areas of the Mississippi coastal plains are mainly pine forests, 
but there are also large areas covered by swamp hardwood forest in the bottom- 
lands adjacent to the major rivers. Pine tree foliage is green during January 
at which time most other vegetation is either dead or leafless. Most hardwood 
trees are leafless during January, and, although there are some evergreen brush 
species in the understory, the hardwood forelts should be spectrally distinct 
from other vegetation that may exist. 
total forest area could be most accurately classified utilizing ERTS data 
acquired in January, and that pine forests could be better distinguished from 
Therefore, it was hypothesized that the 
334 
e 1. Classif icat ions within t ra in ing  sample areas expressed as percentage of 
t o t a l  cells representing a given surface condition c l a s s i f i e d  as per- 
ta ining t o  t h a t  surface condition. 
Surface Condition 
(Land Use) 
Aug. 1972 Jan. 1973 
da ta  da ta  
Total  forested area 92.4 97.8 
Pine f o r e s t  81.4 91.5 
Hardwood f o r e s t  72.7 88.5 
Total  cropland area 
Soybeans 
84.6 84.2 
80.0 - 
Corn 96.0 - 
Exposed s o i l  92.0 - 
Winter ryeerass - 89.1 
Stubble 69.8 
Grass (improved and unimproved 
pasture) 89.0 80.4 
Marsh (non-foresKea weuands) 94.9 67.4 
Water 97.6 98.9 
I n e r t  materials (asphalt ,  concrete, 
m e t a l ,  etc.) 94.9 65.9 
Overall 94.8 89.0 
hardwood f o r e s t s  with the  same data.  
by t h e  c l a s s i f i c a t i o n  r e s u l t s  shown i n  Table 1 which show t h a t  t h e  c l a s s i f i c a t i o n  
within f o r e s t  t ra in ing  sample areas was 92.4% f o r  August da ta  and 97.8% f o r  
January data.  Furthermore, t h e  scorecard c l a s s i f i c a t i o n  within pine f o r e s t  
t r a i n i n g  samples was  81.4% f o r  August data  versus 91.5% f o r  January data.  
The v a l i d i t y  of t h i s  hypothesis i s  indicated 
During August 1972, on the  Mississippi coastal  plains ,  t h e  main a g r i c u l t u r a l  
crop w a s  soybeans, although there  w a s  some corn and some exposed s o i l  i n  cu l t iva ted  
areas. Soybeans and corn are spec t ra l ly  s imilar ,  and both crops are spec t ra l ly  
similar t o  grass  vegetation. However, exposed s o i l  i s  spec t ra l ly  d i s t i n c t  from 
a l l  green vegetat ion during August. 
contains winter ryegrass i n  a green grokiing c o d i t i c s ;  2nd the remainder of the  
cu l t iva ted  area contains s tubble  (dead soybean o r  corn s t a l k s )  o r  dead vegetation 
that  is s p e c t r a l l y  d i s t i n c t  from ryegrass but  spec t ra l ly  s imilar  t o  dead grass  
and dead marsh vegetation. 
During January, some of the  cul t ivated area 
Therefore, i t  was  hypothesized that there  w a s  not  
335 
likely to be a significant difference in land use classification accuracy between 
January data and August data for the total cultivated area, but that the ryegrass 
classification attained with the January data was likely to be more accurate than 
the soybean classification attained with the August data. 
hypothesis is also indicated by the scorecards. 
training samples for cultivated areas was 84.6% for August data and 84.2% for 
January data, but the soybean classification with August data was 80.0% and the 
ryegrass classification with January data was 89.1%. 
The validity of this 
The classification within all 
Marsh vegetation is in a vigorously growing state during August; whereas, 
except for a few evergreen brush species in some areas, marsh vegetation is 
dead during January, and should be spectrally similar to dead grass, stubble and 
leafless hardwood. Therefore it was hypothesized that the most accurate classi- 
fication of marsh vegetation could be attained with August data. The scorecard 
results show 94.4% for August data and 67.4% for January data. The low accuracy 
for the marsh classification with January data is attributed to a similarity be- 
tween a marsh spectral signature and a spectral signature 
under leafless hardwood swamp forest. 
"marsh" training sample cells were classified as "hardwood. " 
for a flooded condition 
In the January classification, 21.7% of 
A computer-derived classification, as used in this study, is based on sepa- 
rating surface conditions that have different spectral characteristics caused by 
differences in reflected energy as measured from above. 
industrial, and residential land uses can be separated from the other land uses 
only in-as-much as their surface conditions with inert materials (asphalt, 
concrete, metal, wood, etc.) are spectrally different from vegetation or other 
material (sand, water, etc.). Residential or urban areas that have foliated 
trees overtopping the buildings as well as lawns and shrubs occupyng surface 
areas as seen from above are likely to be classified as vegetation. 
color assigned to inert materials (asphalt, concrete, etc.) and the colors assigned 
to other surface conditions (grass, trees, etc.) that may be in the urban envlron- 
ment will form color patterns on a color presentation that can be interpreted SO 
as to enable delineations of urban areas, especially to separate urban commercial 
and industrial centers with large concentrations of inert surface materials, 
residential areas with associated vegetation, and other land uses. In this con- 
text, the classification results within training sample areas is meaningless to 
the accuracy of the classification of the total urban commercial, industrial, or 
residential area. However, in-as-much-as the hardwood trees that overtop one- 
story or two-story buildings are leafless and lawn grasses are dead during January, 
a larger portion of the urban areas outside of training sample areas was classi- 
fied as having inert material (asphalt, concrete, etc.) on the surface when 
utilizing ERTS data acquired in January than when utilizing data acquired in 
August when all vegetation is green. 
Urban, commercial, 
However, the 
CONCLUSIONS 
In summary, it was shown that August data yielded the best classification to 
determine the extent of the area occupied by marsh vegetation, soybeans, and ex- 
posed soil; whereas, the January data yielded the best classification of the 
forested areas (as well as separating pine forest from hardwood forest), winter We- 
grass and urban areas. It was also shown that there were no significant differences. 
336 
between the August and January classifications of the 
or water bodies. 
attained for certain surface conditions utilizing data acquired during other 
seasons (e.g. for the total cultivated area during May when most fields are in 
some phase of soil preparation and have no significant amounts of green or dead 
vegetation). If this hypothesis is valid, it may be possible to attain the most 
accurate classification of surface conditions by integrating land use classifi- 
cations made with ERTS data acquired during each season of the year. 
total agricultural area, 
These findings suggest that even better classifications may be 
Work is proceeding to produce a land use classification with ERTS data 
acquired during the spring when nearly all cropland is in some state of soil 
preparation, and during the fall when deciduous species have changed leaf color 
but have not shed their leaves. A comparison of four classifications of data 
acquired during the four seasons will determine the extent to which land use 
classification can be improved by combining two or more classifications for 
different seasons. 
Future work will also include adaptation of software to general purpose 
computers, and establishment of ground truth logistics for the implementation of 
computer implemented land use classifications over areas larger than the 100 
nautical mile by 100 nautical mile area encompassed by this study. 
337 


Computer-implemented land use classification with pattern recognition software and ERTS digital data

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