第30回日本情報地質学会講演会 講演要旨集 037-038頁 2019年

Rice Terrace Extraction from Medium Resolution Satellite Images using
Machine Learning Methods
Hang Thi DO*, Venkatesh RAGHAVAN** and Go YONEZAWA***Graduate School for Creative Cities, Osaka City University,
3-3-138 Sugimoto, Sumiyoshi-ku, Osaka 558-8585, Japan. E-mail: dohanghumg@gmail.com.
** Graduate School for Engineering, Osaka City University,
3-3-138 Sugimoto, Sumiyoshi-ku, Osaka 558-8585, Japan.
.

Key words：Rice Terrace, Support Vector Machine, Random Forest, Neural Network, OBIA

1. Introduction
Rice terrace is agriculture production in hilly areas
which reduces soil erosion and control water resource.
They are historical agricultural landscape, and an
essential factor in the ecosystem conservation. Most ofstudies about rice terrace classification have been usedhigh-resolution remote sensing data which is costly anddifficult to use in monitor rice terrace over a large-scale(Zhang et al., 2017). In recent years, machine learning
methods has used frequently on remote sensing area. In
this study, we evaluated the capability of two medium
resolution remote sensing RapidEye and Landsat images
in rice terrace classification in Lao Cai area, Vietnam.
Pixel-based and object-based approaches were comparedto obtain the best classification result. Feed-forward
neural network (FNN), Random Forest (RF), and Support
Vector Machine (SVM) algorithms were used for riceterrace classification.

The key parameter to partition the image into objects isthreshold of segmentation (T). In order to obtain optimal
threshold of segmentation, Rate of Change of Local
Variance (ROC-LV) was used (Dragut et al., 2010). In thisstudy, thresholds ranged from 0.01 to 0.5, step is 0.01.
Optimal thresholds were list at the first row of Table 1and Table 2.
2.3. Feed-forward neural network
Feed-forward back propagation neural network (FNN) isa well-known model which has powerful computing
capabilities base on the propagation of information
between neurons (Zhang et al., 2016). Input layers arespectral bands of the remotely sensed images. 8 hidden
layers which include 128 nodes for each layer is setup.
Rectifier is chosen as activation function with dropout
ratio of hidden layers is 0.2. L1 and L2 are set as 10-5.
Logloss metric is exploited to decide the best model it canbe reached.

2. Data and Methodology
Figure 1 showed the methodology of the study. Pixelbased and object-based (OBIA) were approached toclassify terrace and no terrace classes from RapidEye andLandsat imageries using FNN, RF and SVM classification
algorithms. Training and validation datasets were used totrain and optimize the models of FNN, RF and SVM
classifiers. Finally, test dataset was employed to assess
the classification accuracy and evaluate the capability ofRE, and LS on rice terrace extraction.
2.1. Data and Study area
Lao Cai area is in North of Vietnam where rice terrace
is the primary, long history agriculture practice. The
remotely sensed data used are 5 bands (blue, green, red,NIR, redegde) of 5-m RapidEye and 6 bands (blue, green,red, NIR, SWIR1, SWIR2) of 30-m Landsat images.
Reference data has collected from Google map in order tobe used as training, validation dataset for classificationand test dataset for accuracy assessment and evaluation.
2.2. Optimal threshold of OBIA
OBIA is generated by image segmentation process.

Figure 1. Flow chart of the study

2.4. Random Forest
Random forest (RF) classifier is an ensemble learning
method that uses a randomly selected subset of training
samples and variables to produces multiple decision trees(Belgiu, 2016). Two parameters of the model were set as:
the number of decision trees (Ntree) equals 500, and thenumber of variables in the random subset at each node(Mtry) is chose as default in caret.

38

2.5. Support Vector Machine
SVM works by finding a hyperplane with the largest
margin in the feature space that separates input data into
target classes (Abe, 2010). In this study, Radial Basic
Function (RBF) kernel of the SVM classifier is used, due
to commonly used and shows an excellent performance ofthe function. Size of subset equals 9.

With object-based approaches, the classification accuracy
achieved the highest values at the first peak of ROC-LVgraph. The classification accuracies crucially declined
from pixel-based to the second peak of ROC-LV graph,then slightly reduced to the lowest accuracies.
Furthermore, three classifiers produced almost similar
accuracy at same approach.

3. Result

4. Discussion and Conclusion

3.1. RapidEye classification
Pixel-based approach showed the most accurate
results in comparison with object-based approach, at
91.9% of FNN classifier, 92% of RF and SVM (Tabel 1).
With object-based approaches, the classification accuracy
achieved the highest values at the first peak of ROC-LVgraph. In general, the values slightly decreased whenthreshold values increased, the lowest accuracies
belonged to very high thresholds. FNN, RF, and SVM
classifiers produced almost same accuracy at sameapproach.

This study presents an evaluation of 5-meter
RapidEye and 30-meter Landsat image on rice terrace
extraction by using FNN, RF, and SVM classifiers at
pixel-based and OBIA approaches. Both remote sensing
imageries showed the highest and almost similar
accuracy at pixel-based approach. At OBIA, theaccuracies decreased when thresholds increased. However,the degree of reduction was more significant at Landsat
data. Also, the difference of accuracy among threeclassifiers was small.
It recommended that both imageries are useful forrice terrace extraction at pixel-based approach, however,
only RapidEye image could be used for rice terrace
classification at OBIA. Furthermore, rice terrace
classification from remote sensing data is not affectedmuch by the classification methods.

3.2 Landsat classification
Overall accuracies of pixel-based approach were at
89.7% of FNN, 89.2% of RF, and 89.9% of SVM, higher
than all object-based classification cases (Table 2).

Figure 1. Rice terrace maps classification of (left) Rapideye and (righ) Landsat at pixel-based
Table 1. Overall accuracy of rice terrace from RapidEye image classification. First row showed threshold of OBIAFNNRFSVM

pixel
91.9
92.0
92.0

FNNRFSVM

pixel
89.7
89.2
89.9

0.06
90.0
90.3
89.8

0.09
88.7
89.2
88.5

0.12
88.1
88.9
88.3

0.14
88.5
88.8
87.9

0.20
87.2
87.8
87.0

0.22
86.6
87.6
86.7

0.25
86.5
87.2
86.5

0.28
86.3
87.3
86.5

0.32
85.9
87.0
86.5

0.37
86.4
86.7
86.3

0.40
85.4
86.6
86.0

0.43
85.4
86.5
85.6

0.45
85.4
86.7
86.3

Table 2. Overall accuracy of rice terrace from Landsat image classification. First row showed threshold of OBIA

0.05
83.3
83.9
83.4

0.09
78.5
78.6
78.8

0.13
77.8
77.8
78.0

References

0.15
77.8
77.4
78.0

0.18
76.1
76.3
76.7

0.23
75.6
75.8
76.3

Abe, S., 2010, Support Vector Machines for Pattern
Classification. Springer, London.
Belgiu, M. and Dragut, L., 2016, Random forest in remotesensing: A review of applications and future directions.
ISPRS Journal of Photogrammetry and Remote
Sensing, vol.114, pp.24–31.
Dragut, L., Tiede, D. and Levick, S. R., 2010, “ESP: A tool
to estimate scale parameter for multiresolution image
segmentation of remotely sensed data”. International

0.27
75.7
75.9
76.3

0.33
75.7
75.9
76.3

0.35
75.4
75.7
76.1

0.37
74.0
74.0
74.5

0.43
74.0
74.1
74.5

0.45
73.7
73.6
74.3

0.47
73.8
73.6
74.3

Journal of Geographical Information Science, vol.24,
no.6, pp.859-871.
Zhang, Li, Zhang, Le and Du, B., 2016, “Deep Learning
for remote sensing data”. IEEE Geosciences andremote sensing magazine, 0274-6638/16.
Zhang, Y., Shi, M., Zhao, X., Wang, X., Luo, Y. and Zhao,Y., 2017, Methods for automatic identification andextraction of terrace from high spatial resolution
satellite data (China-GF-1). International Soil andWater Conservation Research, vol.5, pp.17-25.