International Journal of Remote Sensing
Vol. 27, No. 10, 20 May 2006, 1993–2002

Technical Note
Evaluation of supervised classification algorithms for identifying crops
using airborne hyperspectral data
KAZUO OKI*{, LU SHAN{, TAKUYA SARUWATARI{,TOMOYUKI SUHAMA{ and KENJI OMASA{{Graduate School of Agricultural and Life Sciences, The University of Tokyo,
1-1-1 Yayoi, Bunkyo-ku, Tokyo 113-8657, Japan{Center for Geo-Information Technology Integration, PASCO Corporation,
Higashiyama Bldg, 1-1-2, Higashiyama, Meguro-ku, Tokyo 153-0043, Japan(Received 30 September 2004; in final form 7 September 2005 )Sufficient training data must be acquired to classify areas of interest using a
supervised classification method and hyperspectral data. However, the relatively
small size of agricultural plots in Japan means that there is no training area large
enough to represent a feature of interest. In this study, a new method for
identifying crops using hyperspectral remotely sensed data has been proposed inorder to resolve the problem of identifying training areas in agricultural crops.
This method was then compared with conventional methods. The proposed
method was found to be most effective for identifying crops using hyperspectral
data in an agricultural land area.

1.

Introduction

Remote sensing has shown great promise in identifying the crops grown inagricultural land. The resultant information has been found to be useful in theprediction of crop production and of land use change (Myers 1983, Steve and Clark
1990, Owe and D’Urso 2002, Omasa et al., in press). Furthermore, recent advances inagricultural remote sensing include applications in sustainable agriculture, such as
precision farming, agroforestry, and land conservation. These are closely related toapplications in forestry, ecology, hydrology, and environmental management (Hobbs
and Mooney 1990, Rencz 1999, Owe and D’Urso 2002, Omasa et al., in press).
Advancement in sensor technology has led to development of new tool, i.e.
mapping spectrometry from air- and space-borne platforms (Campbell 1996, Omasa
et al., in press). Although broad categories of interest such as crop, urban, water
bodies, and soil areas can be classified using multi-spectral sensors, such as theLandsat Thematic Mapper (TM) and SPOT High Resolution Visible (HRV),
identification of crops or soils with subtle differences in spectral response pattern
will not be achieved. This is because multi-spectral sensors like TM and HRV have alimited number of spectral channels and coarser spatial resolution. So, imaging
spectrometry with very narrow and several spectral bands may offer potential in thisendeavour. Currently, airborne hyperspectral sensors, which can acquire hyperspectral and hyperspatial imagery, are the most effective way to classify various
crops and soils in agricultural land.
*Corresponding author. Email: agrioki@mail.ecc.u-tokyo.ac.jp
International Journal of Remote Sensing
ISSN 0143-1161 print/ISSN 1366-5901 online # 2006 Taylor & Francis
http://www.tandf.co.uk/journals
DOI: 10.1080/01431160500380455

1994

Kazuo Oki et al.

In studies using hyperspectral imagery data, arid vegetation has been
discriminated (Lewis et al. 2001), and green and dry vegetation components havebeen identified (Datt 2000). Furthermore, Cochrane (2000) has classified vegetation
at species level using hyperspectral signatures. He used the discrimination methods
proposed by Price (1994) to calculate the root-mean-square difference and shapedifference between spectral signatures of a particular vegetation and a reference
species. However, the spectral signatures used were not remotely sensed datameasured by satellite or aircraft; rather, the data were measured by using a portable
spectral radiometer. In addition, these methods were effective for distinguishingspecific types of vegetation from each other, but they have not been applied to
identify various types of vegetation using hyperspectral remotely sensed imagery.
In this study, a new supervised classification method is proposed for identifying
crops at the species level in Japanese agricultural land by using hyperspectral data.
In general, it is important to identify a large number of training sets to classify aparticular crop or soil category by a supervised classification method using
hyperspectral data. However, because agricultural plots in Japan are too small, it isdifficult to find adequate number of sufficiently large training area for each categoryof interest. It calls for development of a new method for accurate classification, evenin the absence of sufficient number of training data in the area of interest. The
proposed method has also been compared with the existing conventional methods.
2.

Study area and data used

2.1

Characteristics of the Miura peninsula

The Miura peninsula is located in the south of Tokyo bay (figure 1) and, because ofits mild climate, has been developed as an agricultural area devoted mainly tovegetable growing. The main crops are radish and cabbage in the winter andwatermelon, melon, and hard squash in the summer. Various fruits, flowers, andanimal products are also produced. In addition, the cultivation of vegetation such as
marigold (Tagetes) has increased recently because this flower is adequate for
agricultural or environmental conservation.
2.2

Hyperspectral data

Figure 2 shows hyperspectral image used to develop a new supervised classification
method and to compare it with conventional supervised classification methods in the
agricultural area of the Miura peninsula. The image was acquired on 27 July 2002.
The data were measured by Airborne Imaging Spectroradiometer for Application(AISA), which is a hyperspectral sensor with a 2-m spatial resolution, developed bySpectral Imaging Ltd. The wavelength range is 0.43–1.0 mm, with a maximum of 512
spectral bands. In this study, 70 spectral bands were used within the range of 0.43–
1.0 mm. The AISA sensor head includes a fibre optic probe (FODIS) for real-timemonitoring of downwelling solar irradiance to calculate the apparent reflectance of
the Earth’s surface (Omasa et al., in press).
2.3

Training and test data for classification

To determine the training and test areas in which to analyse supervised methods
using hyperspectral imagery as shown in figure 2, a ground survey was carried out
on 14 August 2002. In this study, a total of seven categories (maize, watermelon,

Classification methods for a small agricultural land area

Figure 1.

1995

Location of the Miura peninsula in Japan.

tree, marigold, soil 1, soil 2, and building) were used in the evaluation of eachsupervised method. Figure 2(a) and (b) shows the training and test areas of eachcategory, and table 1 shows the number of pixels in each category used in the
training and test data. Because soil 1 and soil 2 differed in their spectral radiance, we
selected these soils as the training data. Figure 3 shows the average spectral radianceof each of the seven categories in the training and test areas.
From table 1, we can see an inability to collect many pixels for each category fromthe agricultural land area of the Miura peninsula. We need a lot of training data inorder to accurately classify land area using conventional supervised methods such as
maximum likelihood classification. In particular, more training data are needed
when supervised methods with hyperspectral data are used (Malek et al. 2002).
3.

Classification methods

Four supervised classification methods—(1) maximum likelihood (MLH), (2)Euclidean shortest distance (MED), (3) shape difference (SD), and (4) MEDSD—were selected in this study to evaluate the accuracy of the classification ofagricultural land using hyperspectral data. MLH and MED have been used asconventional methods for analysing multispectral data such as Landsat TM and
SPOT HRV. SD was recently proposed as a method for hyperspectral data (Price
1994, Cochrane 2000). In this study we propose MED-SD as a new method foranalysing hyperspectral data. The characteristics of these methods are described
below.

1996

Kazuo Oki et al.

Figure 2.
2002.

3.1

Hyperspectral image of agricultural land of Miura peninsula observed on 27 July

MLH

The MLH (maximum likelihood) method has been widely used for remote sensingclassification. In particular, the method has been used to classify multispectral
imagery such as that of the Landsat TM. The principle of MLH is based on Bayes’decision rule. Thus, when spectral radiance data x5(x1, x2…., xn)t with n bands isclassified into k types of categories (w1, w2,…., and wk), the data x is classified into
the category wi, where the value of discriminant function fwi(x) as in the following
equation is smallest.
Table 1. Number of pixels in each category used in the training and test data.
Maize(a) Training data
Number of27
pixelsArea (ha)0.011(b) Test data
Number of41
pixelsArea (ha)0.016

Watermelon

102
0.041
379
0.152

Tree

Marigold

Soil 1

Soil 2

Building

176

73

68

85

106

0.029

0.027

0.034

64

85

0.026

0.034

0.070
139
0.056

100
0.040

0.042
136
0.054

Classification methods for a small agricultural land area

1997

(a)

(b)

Figure 3.

Average spectral radiances of each category in the (a) training and (b) test areas.

Discriminant function:
fwi ðxÞ~ðx{mi Þt Si{1 ðx{mi ÞzlnjSi jð1Þwhere mi is the average spectral radiance of category wi calculated from the sample
in the training area, Si is the dispersion covariance matrix of category i, and t stands
for transpose.
Because the MLH method is based on statistical theory, it has been used well toclassify multispectral imagery. However, it has been reported that an abounding

1998

Kazuo Oki et al.

number of samples of each category in the training area is necessary for accurateclassification using hyperspectral data (Malek et al. 2002). Therefore, it may not beappropriate to use this method for agricultural land, such as that at our study site,with its considerably small numbers of samples in each category (table 1).
3.2

MED

Like MLH, the MED (Euclidean shortest distance) method has been used to classify
multispectral imagery. The value of discriminant function fwi(x) as shown in thefollowing equation is smallest.
Discriminant function:
fwi ðxÞ~ðx{mi Þt ðx{mi Þð2Þwhere x is the measured spectral radiance with n bands, mi is the average spectral
radiance of category wi calculated from a sample in the training area, and t stands
for transpose.
It has been reported that the classification result of MED is better than that of
MLH when there are few samples of each category, because of the simplicity of
MED’s discriminant function, which uses only the mean of a sample in the training
area as statistical information (Fujimura and Tsubaki 1985). Furthermore, it has
been reported that the effects of mixed pixels, which contain various categories
within single pixels, are comparatively small when remotely sensed imagery wasclassified with MED (Ishida and Inamura 2002).
3.3

SD

The SD (shape difference) method has been used to identify categories using
hyperspectral data (Price 1994, Cochrane 2000). The value of discriminant function
fwi(x) as shown in the following equation is smallest.
Discriminant function:
0
1
P
B
C
n xmi
fwi ðxÞ~cos{1 @qﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
ð3Þ
P 2 P 2Axmnn iwhere x is the measured spectral radiance with n bands, mi is the average spectral
radiance of category wi calculated from the sample in the training area.
It is found that SD considers the similarity of spectral radiance to classify eachcategory from equation (3). From equation (3), the spectral radiances of the twocategories have identical shapes when the value of the discriminant function is zero,but these categories are not always the same because various effects, such as theangle of view, atmospheric properties, spectral mixture, and illumination angle canchange the spectral radiance (Price 1994, Cochrane 2000).
3.4

MED-SD

MED-SD is this study’s proposed method to classify categories using hyperspectral
data. It is considered to combine characteristics of MED and SD. In particular,
MED-SD normalizes each value calculated from equations (2) and (3) and adds
them together. The value of discriminant function fwi(x) as shown in the following
equation is smallest.

Classification methods for a small agricultural land area

1999

Discriminant function:
fwi ðxÞ~Di {Dminhi {hminz
Dmax {Dmin hmax {hminð4Þwhere Dmax and hmax are the maximum values of each pixel calculated fromequations (2) and (3), respectively, Dmin and hmin are the respective minimum valuesof each pixel calculated from those equations, and Di and hi are the respective valuesof each pixel calculated from those equations.
4.

Evaluation of methods

4.1

Analytical procedure and evaluation

To compare the accuracy of the four supervised classification methods using
hyperspectral data for agricultural land of the Miura peninsula, a set of sevencategories, namely maize, watermelon, tree, marigold, soil 1, soil 2, and building wasused.
To evaluate the accuracy of each method in seven categories, error matrices havebeen generated, wherein overall accuracy, Kappa coefficient value, the producer’s
accuracy, and the user’s accuracy were computed (Foody 2002, Keuchel et al. 2003,
Maxwell et al. 2004). Overall accuracy and Kappa coefficient value show the
proportion of pixels correctly identified, the producer’s accuracy shows the
proportion of verification pixels of a category correctly classified, and the user’s
accuracy shows the proportion of pixels that are classified correctly. Although theuser’s evaluation of accuracy for each method may be important during theevaluation of the classification map produced over the study site, the user’s accuracy
evaluation is not important when the classification methods are evaluated. This isbecause the user’s accuracy for each category depends on the number of verification
pixels in the test areas. In Japan’s typically small agricultural plots, it is difficult tocollect sufficient numbers of sample pixels in a training and test area, as shown intable 1, to enable the use of the supervised classification method. Therefore, we used
overall accuracy, Kappa coefficient value, and the producer’s accuracy of error
matrixes to evaluate each of the four supervised classification methods for sevencategories.
4.2

Results and discussion

Table 2 shows the accuracy evaluation of the four supervised methods for sevencategories using error matrixes. In table 2, overall accuracy and Kappa coefficientvalues of MLH method were not high in comparison with three methods of MED,
SD and MED-SD. In particular, the producer’s accuracies of MLH were not highfor maize, marigold, and soil 2 because there were few pixels for these categories inthe training area, as shown in table 1. It has been reported that MLH does not work
well when the number of training data are few (Fujimura and Tsubaki 1985, Ishida
and Inamura 2002, Malek et al. 2002). Therefore, MLH should not be used toclassify agricultural land in areas where the collection of training data is difficult,such as the Miura peninsula.
On the other hand, overall accuracy and Kappa coefficient values of MED, SD
and MED-SD methods produced almost the same results with higher accuracy. On
average, the producer’s accuracies of MED-SD were higher for all categories. In
comparison with the results obtained with MED-SD, the producer’s accuracies of2000

Kazuo Oki et al.

Table 2. Accuracy evaluation of the four supervised methods for seven categories.

(a) MLH
Maize
Watermelon
Tree
Marigold
Soil 1
Soil 2
Building
Producer’s
accuracy (%)
Kappa
coefficient

Maize

Watermelon

Tree

1
22
18
0
0
0
0
2.4

0
369
1
9
0
0
0
97.4

0
0
139
0
0
0
0
100.0

0
5
24
35
0
0
0
54.7

0
0
0
0
85
0
0
100.0

0
80
0
0
12
8
0
8.0

0
110
1
0
0
0
25
18.4

100.0
63.0
76.0
79.5
87.6
100
100
70.1

19
350
0
9
1
0
0
92.3

29
5
105
0
0
0
0
75.5

0
0
1
63
0
0
0
98.4

0
0
0
0
85
0
0
100.0

0
0
0
0
0
100
0
100.0

0
0
0
0
0
19
117
86.0

46.1
98.6
99.1
87.5
98.8
84.0
100.0
91.2

35
339
0
0
0
5
0
89.4

0
7
92
40
0
0
0
66.2

0
0
0
64
0
0
0
100.0

0
0
0
0
85
0
0
100.0

0
0
0
0
0
100
0
100.0

1
0
0
0
0
23
112
82.4

48.6
98.0
100.0
61.5
100.0
74.1
100.0
87.5

30
346
0
0
1
2
0
91.3

15
11
113
0
0
0
0
81.3

0
0
0
64
0
0
0
100.0

0
0
0
0
85
0
0
100.0

0
0
0
0
0
100
0
100.0

1
0
0
0
0
24
111
81.6

43.2
96.9
100.0
100.0
98.8
75.8
100.0
90.5

0.583

(b) MED
Maize
41
Watermelon0Tree0Marigold
0
Soil 10
Soil 20
Building
0
Producer’s
100.0
accuracy (%)
Kappa
0.890
coefficient(c) SD
Maize
Watermelon
Tree
Marigold
Soil 1
Soil 2
Building
Producer’s
accuracy (%)
Kappa
coefficient

Marigold Soil 1

User’saccuracy
Soil 2 Building
(%)

34
0
0
0
0
7
0
82.9
0.846

(d) MED-SD
Maize
35
Watermelon0Tree0Marigold
0
Soil 10
Soil 2
6
Building
0
Producer’s
85.4
accuracy (%)
Kappa
0.881
coefficientClassification methods for a small agricultural land area

2001

MED and SD were relatively low for trees. Most of the erroneous classifications
pertained to maize, watermelon and marigold classified using MED and SD. Table 3
shows the values calculated by equation (2) and (3). The similarity between
categories is high when the values of the discriminant function are near zero. Fromtable 3, the values of equations (2) and (3) for maize, watermelon and marigold wererelatively similar to that of trees. Furthermore, it can be seen in figure 3 that tree andmarigold have most similar spectral radiance shapes. On the other hand, theproposed MED-SD method is considered to combine the merits of MED and SD.
Therefore, the classification accuracies for tree were higher using MED-SD.
5.

Conclusions

Four supervised classification methods for identifying agricultural crops using
hyperspectral data were evaluated. The results showed that MLH should not be
used to classify agricultural land where it is difficult to collect training data, such as
Table 3. The values of MED and SD in training and test areas.
Maize

Watermelon

Tree

Marigold

Soil 1

Soil 2

Building

Training area—SD
Maize
0
Watermelon
0.102
Tree
0.229
Marigold
0.232
Soil 1
0.317
Soil 2
0.433
Building
0.525

0.102
0
0.154
0.144
0.408
0.520
0.603

0.229
0.154
0
0.052
0.544
0.660
0.741

0.232
0.144
0.052
0
0.547
0.658
0.740

0.317
0.408
0.544
0.547
0
0.136
0.282

0.433
0.520
0.660
0.658
0.136
0
0.269

0.525
0.603
0.741
0.740
0.282
0.269
0

Training area—MED
Maize
0
Watermelon 14679.26
Tree12371.38
Marigold
30640.34
Soil 110834.57
Soil 218340.68
Building
17121.49

14679.26
08179.1016574.36
24642.67
22533.01
31076.83

12371.38
8179.10019789.59
23135.50
26419.45
29057.59

30640.34
16574.36
19789.59041024.76
37245.8547362.20

10834.57
24642.6723135.50
41024.76
017016.64
7903.43

18340.68
22533.01
26419.45
37245.85
17016.64022690.28

17121.49
31076.83
29057.5947362.20
7903.43
22690.28
0

0
0.063
0.227
0.183
0.337
0.459
0.549

0.063
0
0.209
0.153
0.370
0.488
0.575

0.227
0.209
0
0.067
0.562
0.683
0.772

0.183
0.153
0.067
0
0.518
0.635
0.724

0.337
0.370
0.562
0.518
0
0.154
0.240

0.459
0.488
0.683
0.635
0.154
0
0.216

0.549
0.575
0.772
0.724
0.240
0.216
0

Test area—MED
Maize
0
Watermelon 14783.20
Tree19069.36
Marigold
29131.84
Soil 1
13598.54
Soil 217695.83
Building
17418.37

14783.20
010158.65
15667.97
27552.76
21826.98
30472.83

19069.36
10158.65
011728.31
32609.87
30729.06
36349.59

29131.84
15667.97
11728.31
042569.82
36588.9845804.60

13598.5427552.76
32609.87
42569.82
018397.60
5193.1517695.83
21826.98
30729.06
36588.98
18397.60
018507.12

17418.37
30472.83
36349.5945804.60
5193.1518507.12
0

Test area—SD
Maize
Watermelon
Tree
Marigold
Soil 1
Soil 2
Building

2002Classification methods for a small agricultural land areaon the Miura peninsula, despite this method’s popularity in conventional remotesensing classification.
On the other hand, on an aircraft, the classification accuracies of the proposed
MED-SD method in all categories were higher than those derived using othermethods. In the future, it is proposed to use data dimensionality for improving theclassification accuracy while reducing the time required for analysing hyperspectral
measurement.
References
CAMPBELL, B.J., 1996, Introduction to Remote Sensing (New York: The Guilford Press).
COCHRANE, M.A., 2000, Using vegetation reflectance variability for species level classificationof hyperspectral data. International Journal of Remote Sensing, 21, pp. 2075–2087.
DATT, B., 2000, Identification of green and dry vegetation components with a crosscorrelogram spectral matching technique. International Journal of Remote Sensing, 22,
pp. 2133–2139.
FOODY, G.M., 2002, Status of land cover classification accuracy assessment. Remote Sensing
of Environment, 80, pp. 185–201.
FUJIMURA, S. and TSUBAKI, K., 1985, Classification of multi-spectral remotely sensed
imagery. Journal of the Society of Instrument and Control Engineers, 24, pp. 25–30.
HOBBS, R.J. and MOONEY, H.A. (Eds), 1990, Remote Sensing of Biosphere Functioning (NewYork: Springer).
ISHIDA, H. and INAMURA, M., 2002, Evaluation methods for category classification of mixed
pixel data. Journal of Remote Sensing Society of Japan, 22, pp. 2–11.
KEUCHEL, J., NAUMANN, S., HEILER, M. and SIEGMUND, A., 2003, Automatic land coveranalysis for Tenerife by supervised classification using remotely sensed data. Remote
Sensing of Environment, 86, pp. 530–541.
LEWIS, M.M., JOOSTE, V. and DE GASPARIS A.A., 2001, Discrimination of arid vegetation
with Airborne Multispectral Scanner hyperspectral imagery. IEEE Transactions on
Geoscience and Remote Sensing, 39, pp. 1471–1479.
MALEK, J.E., ALIMI, A.M. and TOURKI, R., 2002, Problems in pattern classification in highdimensional spaces: behavior of a class of combined neuro-fuzzy classifiers. Fuzzy
Sets and Systems, 128, pp. 15–33.
MAXWELL, S.K., NUCKOLS, J.R., WARD, M.H. and HOFFER, R.M., 2004, An automatedapproach to mapping corn from Landsat imagery. Computers and Electronics inAgriculture, 43, pp. 43–54.
MYERS, V.I., 1983, Remote sensing applications in agriculture. In Manual of Remote Sensing,
2nd edn, vol. III, R.N. Colwell (Ed.), pp. 2111–2228 (Falls Church, VA: American
Society of Photogrammetry).
STEVE, M.D. and CLARK, J.A. (Eds), 1990, Applications of Remote Sensing in Agriculture(London: Butterworths).
OMASA, K., OKI, K. and SUHAMA, T., in press, Remote sensing from satellite and aircraft. In
Handbook of Agricultural Engineering, Vol. VI, Information Technology, A. Munack,(Ed). ASAE.
OWE, M. and D’URSO, G. (Eds), 2002, Remote Sensing for Agriculture, Ecosystems, andHydrology III (Bellingham, WA: SPIE).
PRICE, J.C., 1994, How unique are spectral signature? Remote Sensing of Environment, 49, pp.
181–186.
RENCZ, A.N., 1999, Remote Sensing for the Earth Sciences (New York: John Wiley & Sons).