An automated classification procedure is described. The decision rules were developed for classifying an unknown observation by matching its color trend with that of expected trends for known crops. The results of this procedure were found to be encouraging when compared with the usual supervised classification procedures

Tubbs, J. D.

English

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N78-14539
CLASSIFICATION OF LANDSAT AGRICULTURAL DATA BASED UPON COLOR TRENDS
J. D. Tubbs
Department of Mathematics
University of Arkansas
Fayetteville, Arkansas
SUMMARY
Historically, supervised classification procedures are usually "trained"
by photo-interperters who preprocess the data. This preprocessing consists of
identifying and labeling training areas by analysing photo products produced
from the digital Lansat data. An oversimplification of this procedure is that
the photo-interperters label a field according to whether or not its observed
color (determined by the false color IR film product produced from a composi-
tion of landsat bands 4, 5, and 7) coincides with the expected color of known
crops at acquisition time t. Since the success of most, if not all, supervised
classification procedures is highly dependent upon the success of the photo-
interperters, multiple acquisitions obtained throughout the growing season are
desired in order to insure sufficient confidence in the field labeling. In
fact, very few errors are encountered if the photo-interperter has imagery
available at the various growth stages of the crops he is interested in identi-
fying. Based upon this assumption, it seemed feasible to automate a part of
the photo-interperters logic process, that of labeling or classifying fields
(hence pixels) according to their color trend. The purpose of this paper is to
present such a classification procedure. The decision rules have been devel-
oped for classifying an unknown observation by matching its color trend with
that of expected trends for known crops. The results of this procedure have
been found to be encouraging when compared with the usual supervised classifi-
cation procedures.
1. INTRODUCTION
Historically, the majority of crop inventoring of agricultural regions
using Landsat multispectral scanner data have been performed using supervised
classification procedures [MacDonald 1975]. This type of procedure usually
consists of having trained photo-interperters (PI) define and label homogenous
areas found on a film product produced from the digital data. A principle pro-
duct used in this identification is a false color IR produced from a composite
of Landsat bands 4, 5, and 7. In defining and labeling these homogenous areas,
the PI selects areas which are representative of the major crops to be classi-
fied in the test segment. Since the performance of the classifier is highly
dependent upon its training inputs (hence the PI labeling), it is often desir-
able and necessary for the PI to analyze multiple acquisitions of the same test
area throughout the growing season. An oversimplification of this procedure is
that the PI labels a field, crop x, according to whether or not its color at
acquisition time t coincides with the expected color for crop x at time t. By
repeating this process for each acquisition, the PI obtains a degree of confid-
ence in the labeling of the training inputs. In this paper, I have developed
a simple procedure which attempts to automate this portion of the PI logic pro-
cess, that of labeling fields or pixels according to their observed color
trend. The results of this procedure are compared with a supervised classifi-
cation procedure, both in terms of spacial properties (preservation of field
structure) and as crop proportion estimates. Due to the nature of the avail-
able data, I have restricted my attention to the problem of separating wheat
from all non-wheat by using Landsat imagery obtained from at least three dis-
tinct growth stages for wheat.
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https://ntrs.nasa.gov/search.jsp?R=19780006596 2020-03-22T05:46:23+00:00Z
2. PROCEDURE
Engvall, Tubbs, and Holmes (1977) have shown that certain agricultural
crops could be classified according to their temporal trend. In order to det-
ect the temporal trends, it was necessary to transform the original four dimen-
sional Landsat space S^ onto a two dimensional slpace Sp. Then an unknown ob-
servation is classified by matching its temporal trend with the temporal trend
for known crops. In their paper, the transformation was based upon band dif-
ferencing and ratioing, in order to take advantage of the spectral variability
over varying ground targets. In this paper, I have developed a procedure which
is also based upon classifying temporal trends, however I have projected the
original Landsat space SL onto a hopefully more familiary space, one that is
equivalent to plotting points in the standard CIE Chromaticity diagram [ 3 ].
As mentioned in the introduction, the analysis of false color IR film pro-
duct obtained periodically throughout the growing season is usually sufficient
to insure that the PI correctly label the training inputs. This is particu-
larly true if certain auxiliary information (cropping practices, updated wea-
ther reports, etc.) is also available. The PI then labels a field A, crop x,
if the observed color (red on film implies a green-like target, non-red implies
a non-green like target) agees with the expected color for crop x at acquisi-
tion time t. For example, if the PI suspects field A is winter wheat, then he
expects to see a non-red (preemergence), red (emergence to heading), then non-
red (harvest). There will be some variability in this general trend, however
variance could be anticipated according to the auxiliary information. Since
the film product is produced directly from the digital data, it seems feasible
that one could determine directly (without analyzing the film product) whether
or not an observation is red or non-red. Then by repeating this procedure for
each acquisition, one would be able to classify an unknown observation accord-
ing to its agreement or disagreement with the expected trends for known crops.
Mathematically this is equivalent to defining a transformation from the
spectral space SL onto 0 to 1 (0=non-red, 1= red). Let X =(x, ,x_ ,x, ,x. )
represent the four dimensional row vector corresponding to the multispectral
scanner data (Xfce SL) acquired at time t. Due to the lack of variability in
the scanner data and to the inconsistency in the dynamic range of the spectral
bands, it is often necessary to enhance the data before processing the color
product. One such approach is to define a linear map on X by
x. = s.x. + b. for j = 1,2,3,4. (1)
where s j, bj are such that x"j-3sdj=0 and Xj,+3sdj=255, when x j, sdj are the
sample mean and standard deviation for band j and (0,255) represents the dyna-
mic range of the photo processor. Now define the following transformation
it
Y j- A Y4- 4-
* * * * * * *
} ,X« rX-. ) f Dr X-| T X~
Jt 3t 4t 1t 2t 't
where X = (x, ,x_ x, ,x. , b = x, + x2 +x^ and (2)
0 0 0 1/br
A 0 1/br 0 0
1/br 0 0 0
Thus X. is mapped onto Yfc= (y.^  ,y2 ,y^ ) which is equivalent to Yt=(y^ '^2 '^ >r^
since y, +y~ +y-> =1. Note that Yfc is the Trichromatic coefficient [3 ], and
y1 y are the chromaticity coordinates which can be plotted on the CIE dia-
t t
gram. These coordinates can be used to determine whether or not X. is red or
920
non-red when projected onto the color plane S^. Let R denote the red region of
the color plane and NR the non-red region. If (y. ,y. )e R, then let d = 1,
t t
otherwise d = 0. By repeating this process for each acquisition t, t=l,2,...,
n, we have reduced a 4 x n dimensional row vector X = (X^ ,X2,...,Xn) onto a n
dimensional row vector D = (di,d2,... ,dn) , where dj= 1 if the jfc" acquisition
of the unknown observation X is red like on the film product.
3. CLASSIFICATION PROCEDURE.
Suppose that there are m different crops to be classified, denoted by
C1»C2'•••'Cm- Let Gj= the set of Dk's that are to be associated with crop j,j=l,2,...,m. Let X= (X^,X2,...,Xn) be an arbitrary observation to be classi-
fied. Since there are 2n possible outcomes and n is usually small (n_<^ 6) the
matching problem is fairly trivial. For example, assume that n=4, where the
acquisitions are spaced according to the distinct growth stages for wheat. Let
C^ denote the class of pure wheat, C2 denote the class of possible wheat, and
C3 denote the class of non-wheat, where GI={DI>, G2=(D2,D,,D.} and G,= the
remaining 12 outcomes and
Oj^ = (0,1,1,0) ideal wheat (z=6)
D_ = (0,1,0,0) possibly early maturing wheat (z=2)
D, = (0,0,1,0) possibly late developing wheat (z=4)
D. = (1,1,1,0) possibly very early developing wheat
(z=7).
Class C2 is defined to account for the variability of the wheat fields within a
sample segment and the proportion of observations falling into this class could
be weighted according to the nature of the auxiliary information. Since the D
vector is made up of zeroes or ones, there is an easy procedure for matching
unknown observations with the 2n possible outcomes. Let z = d, +2d2+4d3+2n~ld .
Now the comparison of the color response for an unknown observation with that
of known crop consists of comparing integers. That is, in our example if the
z value for the unknown observation was a 6, then it was classified as pure
wheat, if z = 2,4,7 then it was classified as possible wheat and would be
weighted according to the available information, and if the z value was not
2,4,6,7 it was classified as non-wheat.
4. RESULTS
The proposed classifier (color) was applied to 19 different Landsat data
sets collected during the 1974-1975 growing season throughout the major wheat
producing regions of the United States. To aide in the evaluation of this pro-
cedure, the 19 data sets were classified using three different procedures. The
first (RLE) is maximum likelihood classification using any or all of the avail-
able Landsat acquisitions. The results were deemed satisfactory by the PI us-
ing ad-hoc investigation. The second procedure (B104) is also based upon maxi-
mum likelihood classification where the data sets were classified using all
available acquisitions (multitemporal classification). The third procedure is
the non-supervised classification procedure (DELTA) as proposed by Engvall, et
al. (1977). The results (wheat proportion estimates) have been summarized in
Table 1. Table 2 lists correlations and linear regression coefficients.
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Data Set
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
X
5D
Table 1:
COLOR
11.9
15.9
20.0
7.7
14.2
22.6
20.7
9.1
1.6
32.1
15.1
18.9
34.9
9.6
16.0
9.4
23.1
3.4
26.6
16.1
9.0
Wheat Proportion Estimates
Procedures
MLE
18.0
26.0
7.0
2.0
14.6
36.9
8.0
15.3
0
21.3
12.7
.3
40.0
8.5
8.8
13.7
46.7
20.0
18.0
16.7
13.0
B104
12.6
35.2
10.9
7.4
18.7
33.1
13.8
15.3
.2
23.2
2.4
5.3
39.5
8.5
7.7
13.7
12.3
19.3
15.2
15.4
10.7
ORIGINAL PAGE IS
OF POOR QUALITY
DELTA
6.0
**
27.5
15.9
11.3
24.2
19.1
34.5
7.0
29.7
8.7
3.4
45.8
5.1
.5
1.5
54.6
7.4
7.0
17.1
15.7
** Dates were unsatisfactory
Table 2: Correlation and Regression Coefficients
Source
Color x MLE
Color x B104
Color x DELTA
Bo
8.14
10.00
7.69
B,1
.47
.35
.54
R
.46
.35
.41
r
.68
.64
.59
5. CONCLUSION
As mentioned in Engvall, et al (1977), the difficulty in evaluating or
selecting "best" classification results in absence of ground truth information
has been a real problem to remote sensing investigators. This is particularly
true if one wishes to make decisions based solely upon fixed decision rules.
Recently, there has been a trend toward developing simple (minimum setup and
computer time) classification procedures to aid in this decision process. In
this paper, I have introduced such a procedure. One of the main advantages of
this procedure is that the transformation is similar to the one used in pro-
ducing the color film product. This is particularly important when one con-
siders the nature of this transformation. The transformation is many to one,
which means that different points in the spectral space are mapped to the same
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color. Mathematically this is equivalent to saying that the inverse transform-
ation is not unique. This is evident when the PI defines two training areas
as identical crops, only to find that the classifier finds that they are enti-
rely different. This problem is particularly apparent in defining representa-
tive multitemporal training areas. For this reason I wanted to classify a
sample segment using the same space in which the training areas were defined
and labeled. It was my hope that the color process (hence the proposed classi-
fier) could be used to separate major crop types (wheat from non-wheat), where-
as it may not be able to distinguish different varieties of wheat.
The preliminary results of this investigation have been encouraging.
There seems to be a very good agreement between the classification results and
the PI labeled training areas. I stated in the introduction that I wanted to
compare this procedure with some of the usual procedures using both spacial
properties and proportion estimates. Due to lack of resources, I was not able
to display any of the class maps generated using this procedure, however they
were very satisfactory (comparison of computer printer plot with 5x6 color
film product). The proportion estimates also seemed satisfactory, although
one must always guess at what is a satisfactory answer.
REFERENCES
(1) Engvall J.L., Tubbs J.D., and Holmes Q.A. (1977), "Pattern Recognition of
Landsat Data Based Upon Temporal Trend Analysis" to appear in Journal
of Remote Sensing of the Environment.
(2) MacDonald R.B. et al (1975), "The Use of Landsat Data in Large Area Crop
Inventory Experiment (LACIE)," Proceedings of the Symposium on Machine
Processing of Remotely Sensed Data, West Lafayette, Ind. pp. IB-(1-23).
(3) Wenderoth, et al (1972) "Multispectral Photography for Earth Resources"
Science Engineering Research Group Long Island University. Greenvale,
New York.
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Classification of LANDSAT agricultural data based upon color trends

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