OPTIMAL HYPERSPECTRAL CLASSIFICATION FOR PADDY FIELD WITHSEMISUPERVISED SELF-LEARNING
Taichi Takayama1,2, Naoto Yokoya1, and Akira Iwasaki1
1Research Center for Advanced Science and Technology, the University of Tokyo, Japan2
Mitsubishi Research Institute, Inc., Japantaichi@sal.rcast.u-tokyo.ac.jp
ABSTRACTMonitoring and management of paddy fields are one of key
elements for not only stable production but also ensuring
national food security. Classification of growth stage withremote sensing data is expected to be a highly effectivesolution, which can capture large area in one time observation.
In general cases, a pixel-based classification is one of themost attractive choices. However, acquiring enough numberof field survey plots for the classification is not easy from theaspect of consumed time and cost. This problem can impact
negatively on the accuracy of classification map. In this paper,
we propose semisupervised classification method
considering characteristic of paddy field in order to provide
an optimal classification map with hyperspectral data.
Index
Terms—
Hyperspectral,semisupervisedclassification, growth stage, sparse discrimination analysis,
paddy
1. INTRODUCTION
Remote sensing in precision agriculture is one of strong
support tools for crop monitoring and management. Fromstable production by individual farmers to national foodsecurity in government level, the accurate monitoring andmanagement are very useful. Especially remote sensing data
enables us to know the condition in large area in one timeobservation. There are many researches for monitoring cropconditions with multispectral data, which have limitations inproviding accurate estimates. This limitation has motivatedto use hyperspectral data for the crop monitoring. Hyper
spectral data has rich spectral information which is strong
tools for classification of land use, growth stage, etc. In thenear future, some organizations plan to launch spaceborne
hyperspectral sensors, such as HISUI by Japan Space
Systems and EnMAP by DLR.
Our main target of this study is to provide growth stage
maps with high accurate classification performance, using
hyperspectral data. In the past more than 20 years, there weremany researches on crop monitoring using hyperspectral data[1]. Statistical techniques with hyperspectral data foragriculture might be classified in some categories; predictive
spectral indices with only two to four narrow bands [2],chemometric analysis such as partial least squares, principal
components analysis, etc., machine learning techniques [3]such as support vector machine, neural network, etc.
Especially, this machine learning techniques provides good
performance. However, accurate crop forecasting models
need enough amounts of training samples. In many cases,
there is a concern about the small-sample-size problem withhigh-dimensional data due to limitation of field samples. This
problem can be caused of the complexity of prediction
models, resulting in the poor performance caused by modeloverfitting. The importance of crop forecasting under thelimitation of the training data are shown in the report of
GEOSS [4]. In this study, we used the sparse regularization
method which has high generalization capability and canselect limited and important bands for classification in orderto solve the ill-posed problem [5]. Moreover, focusing on thecharacteristics of reflectance in the paddy field, not only
spatial information but also semisupervised classification
techniques are used in our classification scheme for
improving the performance.
2. SURVEY AREA AND DATA SET
The study area is located in Karawang which is well known
as major granaries in West Java area. Dual and triple croppingof rice is common trend in these areas. Airborne
hyperspectral data were acquired on 13th July 2011 and fieldsurvey for training data of classification in the study area wasconducted on June to July 2011. The airborne hyperspectral
sensor, HyMAP, has 126 bands (450nm - 2480nm) with 4.2
m/pixel. All the field survey points are located in those
captured remote sensing image. However, some survey plots
locating under clouds in HyMAP images were eliminated inthis study. Therefore, total number of survey plots used in thisstudy is 78 quadrats. Growth stage of each quadrat isclassified in 9 classes, which is define by International Rice
Research Institute (IRRI). Table 1 shows the details

information about the defined growth stage. Since not enoughnumber of quadrat in some growth stage such as vegetative
early, reproductive mid, mature grain were obtained in thefield survey, we focus on high accurate classification forremaining 6 classes.
Table 1. Growth stage defined by IRRIearly

midlateSeeding

Tillering

Stem elongation

Vegetative

Reproductive
Panicle initiation
to booting

Heading

Flowering

Milk grain

Dough grain

Mature grain

Ripening

3. METHODOLOGY
Total processing scheme of this study is shown in Fig. 1. Our
proposed methodology consists of four parts described in thefollowing sections.
Input data
Obtained
Hyperspectral
raw data

Field survey
data

Preprocessing

High pass filter

Reflectancedata

Morphologicalopening

Pixelwiseclassification

Segment image ofeach paddy field

Pixel-basedclassification
map

Majority voting withineach segmentation

Selection of additionaltraining data

Semisupervisedclassification

Output data

Classification
mapFig. 1. Processing scheme
3.1. Preprocessing
In order to convert radiance data to reflectance dataconsidering atmospheric effects, atmospheric correction isapplied to the obtained HyMAP data with ATCOR-4.
Moreover, we select 86 bands of the corrected HyMAP databy eliminating 40 bands which are low S/N bands andabsorption bands of O2, H2O and CO2.

3.2. Pixelwise classification
In this research, sparse linear discriminant analysis (SDA) [5],which is a classification method using sparse regularizationtechnique, is applied to the hyperspectral data for theclassification of growth stage. Sparse regularization has thegeneralization capability and can select limited and importantbands for the classification during the process. In this study,
we compare the generalization capability between SDA andSVM as a typical classification method.
3.3. Post classification
3.3.1. Segmentation of each paddy fieldIn general, the growth stage is almost the same inside eachsegment. This can be confirmed from the results of fieldsurvey data. Therefore, the segmentation of each paddy field
has an important role for classification, especially under thecondition that the number of field survey plots is limited. In
this study, we focus on the specific reflectance distribution ofpaddy field which consists of a square shaped flat reflectanceregion and a line shaped road. Spectral difference between
these two elements is exist. In order to detect this spectral
difference and the reflectance flatness of paddy field, highpass filter is adapted to the hyperspectral data. Furthermore,morphological opening with some typical squared disks sizes
is used for keeping only the squared shaped objects similar to
paddy field shapes. After that, watershed segmentation isapplied to the morphological processed image in order toprovide optimal segmentation fitted to shapes of individual
paddy field.
3.3.2. Majority voting within each segment
In each segment, multiple classes from pixelwiseclassification may be combined. As we mentioned before,
since the growth stage inside each segment is almost same ingeneral, we apply the majority voting method to decide anoptimal class. On the other hand, non-segmented regions are
not applied the majority voting.
3.4. Semisupervised self-learning with segmentation map
Under the condition that the number of training data is notenough for classification with high robustness, increasing thetraining data in processing scheme is one of the key toimprove the accuracy and robustness. In order to solve thisproblem, some studies conducted semisupervisedclassification method, especially semisupervised selflearning in which additional training data are decided byusing neighbors of labeled samples and pixelwiseclassification map [6]. In this study, we focus onsegmentation of paddy field and the results of the majority
voting. During the process of majority voting within eachsegment, if almost all the classifiers agree, we can decide thatthe results of majority voting provides a corresponding classlabel with high reliability to the segment. On the other hand,
if most of the classifiers don’t agree, the results of majority
voting may not be enough reliable. In the high reliable case,the minority classes can be identified as wrong results derived
from luck of optimal training data. Therefore, these pixels
having minority classes are added to training data of thepixelwise classification with a correct label which is a majorclass in the segment in order to reinforce performance of theclassification. This process can increase the training data withhigh reliability. Fig. 2 shows the graphical examples.
Hyperspectral image
with labeled sample

Classification map
with segmentation
Segmentation
image
classification
A
Hyperspectral image
with new candidate samples

4.2. Semisupervised classification
Class distribution
in “segment A”
1 2 1 1 1
1 1 1 2 1
1 1 1 1 11 1 1 3 1
1 1 1 1 111
Add the new samples toexisting samples

1Table 3. Comparison between classification models
Closed TestOpen Test
SVMSDASVM
SDA
mid 87.5% 93.8% 87.0% 86.5%
Vegetativelate 59.6% 71.2% 56.0% 64.9%
early 88.1% 95.2% 83.1% 80.2%
Reproductive
late 63.6% 93.2% 53.8% 85.9%
early 93.4% 90.8% 92.8% 85.3%
Ripening
mid 85.2% 92.6% 81.5% 76.8%
Overall accuracy
80.6% 89.3% 76.3% 79.4%

If majority class “1” is highpercentage in this segment

Proposed segmentation method for paddy field wasconducted to the hyperspectral image. In this study,
considering the local condition such as typical size of paddy
field and comparing individual bands, we selected 7 x 7
window size of the high pass filter and apply this filter to band
110 (center wavelength is 2,265 nm) of the hyperspectral data.
Fig. 3 shows the obtained segmentation map.
(a)

(b)

Fig. 2. Graphical example of self-leaning process
4. RESULTS AND DISCUSSION
4.1. Pixelwise classification
The 78 surveyed quadrats were classified in 6 classes. Table
2 shows the number of training data in each class.
Table 2. Number of training samples by growth stage
GrowthVegetative
Reproductive
Ripeningstagemidlate
early
late
early
midNumber
1652844476
27For comparing accuracy of the classification between
SDA and SVM, “Closed Test” and “Open Test” wereconducted. “Closed Test” uses all the data for both training
data and test data. In “Open Test”, every pixel in four-fifth of
all the quadrats in each class is used for training data and theevery pixel in remaining one-fifth quadrats is used for test
data. In this study, combination of training and test quadrats
was decided randomly in 100 times.
The results of “Closed Test” and “Open Test” are shownin Table 3. From this results, SVM provides high accuracy insome classes. However, in overall accuracy, SDA provides
better performance than SVM. This means SDA has enoughpotential in terms of generalization.

Fig. 3. (a) Reflectance data (band 110), (b) segmentation of
paddy field
Moreover, pixel-based classification map by using SDA
shown in Fig. 4(a), which shows noisy classification pattern,
is improved by the majority voting with the pixelwiseclassification map and the segmentation map, as shown in Fig.
4(b). However, some areas in the corrected image have little
consistency with neighbor pixels.
To improve this phenomenon, semisupervised selflearning with the pixelwise classification map, the corrected
map provided by the majority voting, and the segmentation
map, is applied For selection of the segmentation with highreliability, a threshold is necessary for ratio of pixels with amajority class to all pixels in the segment is set to 80 %. The
obtained classification map is shown in Fig. 5(a), and Fig.
5(b) is the results provided by applying majority voting to theclassification map. For comparison between with and without
semisupervised self-learning, Fig. 5(c) and (d) show thoseextended images. These results show our proposed methodshave better consistency with the neighbor pixels and localconditions.
(a)

(b)

Vegetative mid
Reproductive late

Vegetative late
Ripening early

Reproductive early
Ripening mid

Fig. 4. Classification map (a) pixelwise classification, (b)applied majority voting
(a)

(b)

showed high consistency with the neighbor pixels and localconditions. The number of field survey plots as training andtest data was enough for evaluation of pixelwise classification,
however validating the accuracy of our proposedsemisupervised classification needs more field survey plotsor additional GIS data. Moreover, we understood radiometricdistortion in each stripe data obtained by not only airborne
hyperspectral sensor and radiometric adjustment between
those stripe data during mosaic process have much impact toaccuracy of classification. On the other hands, recently
hyperspectral data captured by airborne or spaceborne sensor
but also sensors mounted on UAV is getting to be available
for research. The use of UAV has potential for making easyto collect data and decreasing the cost for survey. Our future
work is to challenge the problems by using those benefits ofUAV observation.
ACKNOWLEDGEMENT
Hyperspectral data is provided by Japan Space Systems. This
work is carried out under the contract with National Instituteof Advanced Industrial Science and Technology for theHyperspectral Imager Suite (HISUI) project.
6. REFERENCES[1] D. J. Mulla, “Twenty five years of remote sensing in precision
agriculture: Key advances and remaining knowledge gaps,” Biosyst.
Eng., vol. 114, no. 4, pp. 358–371, Apr. 2013.

(c)(d)[2] P. S. Thenkabail, R. B. Smith, and E. De Pauw, “Hyperspectral
Vegetation Indices and Their Relationships with Agricultural CropCharacteristics,” Remote Sensing of Environment, vol. 71, Issue 2,
pp. 158-182, Feb. 2000.
[3] J. M. Bioucas-dias, A. Plaza, G. Camps-valls, P. Scheunders, N.
M. Nasrabadi, and J. Chanussot, “Hyperspectral Remote Sensing
Data Analysis and Future Challenges,” Geoscience and Remote
Sensing Magazine, IEEE, vol.1, no.2, pp.6-36, June 2013.

Reproductive earlyRipening mid[4] J. Gallego, M. Craig, J. Michaelsen, B. Bossyns, and S. Fritz,“Best practices for crop area estimation with Remote Sensing,” JRC
Scientific and Technical Reports, 2010.

Fig. 5. (a) Semisupervised classification map, (b) appliedmajority voting, (c) an extend image of Fig. 4.(b), (d) anextended image of (b)[5] L. Clemmensen, T. Hastie, and K. Ersboell, “SparseDiscriminant Analysis,” Technical report, IMM, Technical
University of Denmark, 2008.

5. CONCLUSION[6] I. Dópido, S. Member, J. Li, P. R. Marpu, A. Plaza, S. Member,J. M. B. Dias, and J. A. Benediktsson, “Semisupervised SelfLearning for Hyperspectral Image Classification,” Geoscience andRemote Sensing, IEEE Transactions on, vol. 51, no. 7, pp. 4032–4044, 2013.

Vegetative mid
Reproductive late

Vegetative late
Ripening earlyThis paper has presented the efficiency of semisupervised
self-learning for paddy field analysis with hyperspectral data.
In the pixelwise classification, SDA performed high accuracy
and generalization as shown in the results of “Open Test”.
Furthermore the post classification, such as majority votingwith segmentation map, and semisupervised self-learning