An urban area in central Pennsylvania and the surrounding locality were investigated separately at first by photointerpretation of ERTS-1 imagery and by computer processing of MSS tapes. Next the photointerpretation and processing were coordinated. The results of the cooperative effort of photointerpreters and computer processing analysts were much improved over independent efforts. It was found that single frames of U-2 photography could be projected onto printer output maps with little recognizable distortion in areas 10 to 25 cm square. In this way targets could be identified for use as training areas for computer processed signature identification. In addition, at any stage of category mapping, the level of success in correct classification could be assessed by this method. The results of the classification of the study area are discussed

Applegate, D. N.

Bolling, N.

Borden, F. Y.

Weeden, H. A.

English

NASA Technical Reports Server

Paper L 17 
INVESTIGATIONS OF AN URBAN AREA AND ITS LOCALE USING ERTS-1 
DATA SUPPORTED BY U-"PHOTOGRAPHY" 
H. A. Weeden, F. Y .  Borden, D. N. Applegate, and N. Bolling, Office fur Remote 
Sensing of Earth Resources, The Pennsylvania State University, University Park, 
Pennsylvania 
ABSTRACT 
An urban area i n  cent ra l  Pennsylvania and 
the  surrounding l o c a l i t y  were invest igated 
separately a t  f i rs t  by photointerpretat ion 
of ERTS-1 imagery and by conputer'processing 
of MSS tapes. Each of these independent 
approachs had shortcomings Next the  photo- 
i n t e rp re t a t ion  and processing were coordinated, 
The r e s u l t s  of the  cooperative e f f o r t  of 
photointerpreters  and computer processing 
analysts  were much improved over independent 
e f f o r t s .  It w a s  found that s ing le  frames of 
U-2 photography could be projected onto 
p r i n t e r  output naps with l i t t l e  recognizable 
d i s t o r t i o n  i n  areas 10 t o  25 cm square. I n  
t h i s  w a y  t a r g e t s  could be iden t i f i ed  f o r  use 
as t r a in ing  areas f o r  computer processed 
s ignature  iden t i f i ca t ion .  I n  addi t ion,  a t  
any stage of category mapping, the  l e v e l  of 
success i n  correc'c c l a s s i f i c a t i o n  could be 
assessed by this  method. The r e s u l t s  of t he  
c l a s s i f i c a t i o n  of the study area a re  discussed. 
The s t a t e  of t he  a r t  of producing thematic land use 
maps by d i g i t a l  processing and analysis  of  mult ispectral  
scan da ta  from ERTS-1 by the  Office f o r  Remote Sensing of  
Earth Resources a t  The Pennsylvania S t a t e  University as of  
March 1, 1973, i s  the  concern of t h i s  report .  The geo- 
graphic area of t he  study is  a port ion of East  Pennsboro, 
Hampdon, and Lower Allen townships i n  Cumberland County 
j u s t  across the  Susquehanna River from Harrisburg, 
Pennsylvania. %%.e area i s  especial ly  well su i ted  f o r  the  
study because it exhib i t s  many t e r r a i n  and cu l tu ra l  fea tures  
most r ead i ly  recognizable on ERTS i m a g i n g  systems. The a rea  
i s  rap id ly  developing with a var ie ty  of  t a r g e t s  including 
1015 
https://ntrs.nasa.gov/search.jsp?R=19730019580 2020-03-17T08:49:29+00:00Z
the  Susquehanna River, Conodoguinet Creek, rail marshall ing 
yards, urban and suburban areas,  i n d u s t r i a l  developments, 
agr icu l tura l  regions,  f o r e s t ,  as well as a newly developed 
system of i n t e r s t a t e  highways. 
i den t i f i ed ,  provide fixed points  of  reference between which 
items of l e s s e r  defini-cion can be invest igated and mapped, 
The rapid growth i n  the  area provides an opportunity f o r  
evaluating the  temporal dimension offered by ERTS data. 
I n i t i a l  work a t  Penn S t a t e  exposed the  l imi t a t ions  of 
machine processing alone as well a s  the r e s t r i c t i o n s  of  
visual  photointerpretat ion alone from ERTS scenes. Hence, 
this paper emphasizes the development of a system combining 
the s t rengths  of each method. 
on a CalComp Drum P1otter. l  MSS bulk ERTS-1 da ta  tapes 
were processed on an IBM system 370/165 using a Remote 
Job Entry terminal. The programs used are described i n  a 
paper by Borden (1972). SupporTing ground t r u t h  used f o r  
ve r i f i ca t ion  cane from the 7-l/2 minute quadrangle map 
(1:24,000 scale)  of  Harrisburg-West prepared by the  U. S. 
Geological Survey from a e r i a l  photographs taken i n  1961 and 
f i e l d  checked i n  1963. Culture as of  1961 required supple- 
mental d a t a  t h a t  w a s  provided by inf ra red  photography taken 
from U-2 a i r c r a f t  on Ju ly  26, 1972. The photos are  of  70 mm 
format with a scale  of 1:445,000. A s  character maps were 
produced by the  high-speed p r i n t e r ,  they were ver i f ied  f o r  
accuracy by project ing The U-2 photographic transparency onto 
the  map using a 3-1/4" x 4-1/41f Model D American Optical 
Company s l i d e  projector .  
generated, character map prevented proper r eg i s t r a t ion  of 
the  projected photo over the  e n t i r e  area of the map. T h i s  
required a distribu-cion of  pos i t ive ly  iden t i f i ed  t a rge t s  
throughout the  area s o  t h a t  adjacent portions of the scene 
could be sequent ia l ly  brought i n t o  proper r eg i s t r a t ion .  
Figures 2, 3, and 4 were produced from ERTS image 
number 1080-15185 of  October 11, 1972. A s  bulk MSS tapes 
were received from NASA, they were subset t o  produce work- 
ing tapes f o r  selected portions of the image with guidance 
f o r  the  scan l i n e  and element numbers provided by an Ozalid 
p r i n t  from any channel of the ERTS pos i t i ve  transparency. 
Two preliminary maps a re  produced f o r  review of definable 
Such t a r g e t s ,  properly 
The maps shown i n  Figures 2, 3 ,  and 4 were produced 
Scale d i s t o r t i o n s  of the computer- 
'These maps were presented i n  t h i s  form f o r  the  
convenience of  publication. The normal form of map presen- 
t a t i o n  i s  a character map prepared on the high-speed p r in t e r .  
1016 
boundaries. One shows var ia t ion  i n  overa l l  ref lectance o f  
t he  scene that i s  s imi la r  t o  gray sca le  maps i n  other 
systems, The second map i s  prepared t o  i d e n t i f y  loca l  
areas of  spec t ra l  uniformity. I n  some scenes the  loca t ion  
of clouds and t h e i r  shadows are  noted t o  avoid misinterpre- 
t a t i o n  as ground fea tures .  
a re  completed, one i s  ready t o  proceed as indicated i n  the 
f l o w  char t  i n  Figure 1. 
areas f rom the  t w o  preliminary maps. Select  t he  most  
e a s i l y  recognized t a r g e t s  from spec t r a l ly  homogeneous areas 
with pos i t i ve  widely separated geographic locations.  The 
Susquehanna River i s  an excellent example of such a ta rge t .  
Clusters of characters of  l e s s e r  a r ea l  extent  on the 
spec t r a l ly  uniform map are  next persued. Mean vectors a re  
computed f o r  the t r a in ing  areas using the  four channels t o  
e s t ab l i sh  spec t ra l  signatures.  The euclidean distance of 
separation f o r  p a i r s  of mean vectors a re  computed and pr inted 
out as shown i n  Table 1 ( t o  be re fer red  l a t e r ) .  A s  the  
character maps a re  produced, they a re  ver i f ied  as described 
above by project ing the U-2 photos on the  map. The maps m a y  
be produced i n  separate categories f o r  ease of  checking. 
Figure 2 shows such a map. Note first. the  e a s i l y  recognized 
category of  r i ve r  i n  the upper r i g h t  with the meandering 
creek entering from the l e f t .  T h i s  map shows the  categories 
of open f i e l d s ,  f o r e s t ,  and golf course i n  addition t o  water. 
Areas ve r i f i ed  by U-2 photo cor re la t ion  a re  outlined with 
heavy l i n e s .  T h i s  process leads t o  the  de f in i t i on  of l e s s e r  
areas now geographically fixed and in te rpre ted  on the U-2 
photograph. The number and var ie ty  of  t a r g e t s  i s  expanded 
both with respect  t o  type and extent of geographic separation. 
T h i s  i s  a cyc l ic  operation, as indicated i n  the flow diagram, 
t h a t  continues u n t i l  the  mapper f e e l s  he i s  ready t o  advance 
t o  the  next stage. 
When these preliminary s teps  
F i r s t  l eve l  mapping i s  s t a r t ed  by choosing t ra in ing  
Second l eve l  mapping exploi ts  a computer program using 
c l u s t e r  analysis  developed by Turner (1972). T h i s  program 
works where uniform t ra in ing  areas cannot be found because 
of  spec t ra l  nonhomogeneitg o r  small area. Figure 3 w a s  
g rea t ly  benefited by this technique. The signatures f o r  
some suburb, old suburb, confusion suburb, and indus t r i a l  
and f o r  a l l  parking l o t s  and concrete were thus developed. 
T h i s  map w a s  a l s o  ve r i f i ed  by cor re la t ion  with U-2 photo- 
graphic project ion and the t a rge t s  successfully defined so 
marked. The category "confused suburb" comes about because 
of i t s  lack of pos i t i ve  separation from ag r i cu l tu ra l  f i e l d s .  
When one considers the  mixed character of a suburb consist- 
ing of  r o o f s ,  lawns, t r e e s  and shrubs, and s t r e e t s ,  it i s  
easy t o  see how some of  these combinations might for tu i tous ly  
1017 
compare t o  a mix of s m a l l  f i e l d s  o r  long, narrow f i e l d s  
r e l a t e d  t o  contour s t r ipping.  Reference t o  Table 1 shows 
that The signatures f o r  a category of  "confused suburb" 
( the = symbol) and that  f o r  a f i e l d  (the - symbol) does 
not have a wide separation. T h i s  separation i s  the euclidean 
dis tance of separation of the  vectors representing the 
p a r t i c u l a r  t a rge t ,  
Categories a re  separated f o r  mapping on the basis of 
some m i n i m u m  distance.  When t h i s  dis tance i s  too s m a l l ,  
confusion may r e s u l t .  For example, the dis tance between 
the "confused suburb" signature and the  f i e l d  signature i s  
r e l a t i v e l y  s m a l l ,  1 . 1 ,  thus confusion between these cate- 
gor ies  could be anticipated.  I n  cont ras t ,  the separabi l i ty  
of  r i v e r  water ( the W symbol) f rom every other category i s  
qui te  la rge  implying no problem i n  c lass i fy ing  r i v e r  water 
correct ly .  I f  a mixture of  characters i s  pr inted i n  an 
area,  i t  may be desirable  t o  keep the  separate signatures,  
but lump the area. On the other hand, the signatures may 
be blended t o  produce one character, f o r  example, i n  the 
two i n d u s t r i a l  signatures ( the + symbols) that a re  quite 
separate,  17.7, but  that occur together i n  a mixture f o r  
many i n d u s t r i a l  areas. These separate signatures must be 
preserved, but would be assigned the same mapping s-ymbol. 
Judgment should be used here. More work i s  s t i l l  required 
on the category cal led highways. It comes up well i n  
areas  of  high sub j ect-to-background con t r a s t ,  but i s  weak 
i n  urban and suburban areas. Perhaps the mapper w i l l  have 
t o  produce highway networks by the judicious connection of 
scat tered symbols. T h i s  i s  not a sa t i s f ac to ry  procedure. 
Other spec t r a l ly  nonhomogeneous targets definable by 
c l u s t e r  analysis  that are  d i f f i c u l t  but  worthy of g rea t  
e f f o r t  a re  quarr ies  and s m a l l  streams. Note again the 
cyc l ic  procedure through l eve l  two. 
The th i rd  and f i n a l  l eve l  of mapping i s  now approached. 
It may be that t o o  many categories have been defined and the  
r e su l t i ng  map output i s  t o o  ref ined o r  t o o  confusing t o  
the  reader. Stray symbols can be suppressed. A careful  
review of the map objectives should serve as a guide t o  the 
f i n a l  number of categories t o  be mapped. When a l imi t ing  
goal i s  establ ished,  then the f i n a l  cycle can be cut  o f f .  
It should be noted that the  mapping program includes a 
percent of the area mapped f o r  each category. T h i s  makes 
it easy t o  express the area of  each category when the t o t a l  
area bounded i s  known. Figure 4 i s  the f i n a l  presentation, 
T h i s  i s  a combination of  the f irst  t w o ,  However, i n  order 
t o  make the map readable, the fo re s t  area w a s  subdued so  the  
blank areas a re  f o r e s t  and unclassif ied.  The hope of 
1018 
Table 1. Distances of separation f o r  categories.  
1 w  2 =  3 -  4 -I 5 '  
1 w  0 .o 31 .U 31.5 1 4.. 2 31 ,I!. 
2 =  31 .8 0.0 1 .I 18.3 14.2 
3 -  31 - 5  1.1 0.0 18.0 13.U 
4 +  14.2 18.3 18.0 0.0 17-7 
5 "  31.4 14.2 13.8 17.7 0.0 
diminishing -che unclassif ied group may r e s t  on the merging 
of  data from t w o  scenes f o r  d i f f e ren t  seasons of  the year, 
Conclusions t h a t  can be reached from the work on these 
maps are  as f o l l o w s :  The categories of water, f o r e s t ,  high 
densi ty  suburbs, o l d  suburbs, i n d u s t r i a l ,  parking l o t s  and 
concrete are  reasonably well defined. The categories of 
agr icu l tura l  f i e l d s ,  highway, and low densi ty  suburbs need 
more study. Some items highly desirable  i n  land use mapping 
m a y  not be obtained because of s ize  o r  l o w  subject-to- 
background contrast ,  The temporal aspect of the da ta  has 
not ye t  had su f f i c i en t  opportunity t o  be exploited. The 
combined e f f o r t s  of  i n t e rp re t a t ion  of  ERTS images, air- 
craft  underfl ight photographs, and computer-generated, 
character maps has the  poten t ia l  t o  generate land use maps 
of  high quali ty.  
LITERATURE CL TED 
Borden, F. Pates. 1972. A Digi ta l  Processing and Analysrs 
System f o r  Multispectral  Scanner and Similar Data. I n  
Remote Sensing of Earth Resources, Vol .  I. Edited by 
F, Shahrokhi. The University of Tennessee, Space 
I n s t i t u t e ,  Tullahoma, Tennessee. pp. 481-507. 
P i n e r ,  B. J. 1972. Cluster Analysis of Multispectral  
Scanner Remote Sensor Data. I n  Remote Sensing of 
Earth Resources, Vol. I, Edited by F. Shahrokhi. 
The University of  Tennessee, Space I n s t i t u t e ,  
'Pullahoma, Tennessee. pp. 538-549. 
ACKNOWLEDGEMENT 
Financial support for this project was  furnished by NASA on ERTS Contract 
NAS5-23133 and by the MITRE Corporation on Contract N 35490. 
1019 
FIBT 
LEVEL 
W I N G  
SECOM) 
LEVEL 
MAPPING 
T H I m  
LD(EL 
!WING 
SELECT TRAINING AREAS (TA’S) 
BASED ON COMPUTER PROCESSING 
COMPUTE SIGNATURES FOR TA’S - 
CLASSIN AMI MAP 
COMPARE MAPPING u I s WITHIN TA’s 
+ 
WITH PROJECTED 1-5 PHOTOGRAPH 
EXTEND THE NUPBER AM) VARI 
TARGETS WITH GUIDANCE FROM / 
USE CLUSTER ANALYSIS TO DEFINE 
S W E C T  TO DEFINITION BY 
REVIEW CATEGORIES - CONSIDER 
COM3INING AREAS 
Figure 1 
Mapping ac t iv i ty  f l o w  chart. 
1020 
1021 
Creek &$f 
River @:' 
Suburb -= 
Old suburb @@ 
I n d u s t r i  a1 
Agriculture $111!?! 
Fores t ,  other Slark 
Suburb confusio,r 
Figure 4. Class i f i ca t ion  map of  a11 ca tegor ies .  
1022 


Investigations of an urban area and its locale using ERTS-1 data supported by U-photography

Abstract

Similar works

Full text

Available Versions

NASA Technical Reports Server