第32回日本情報地質学会講演会 講演要旨集 009-010頁 2021年

機械学習による Sentinel-2 および Landsat-8 画像の土地被覆分類比較：
ベトナム・ラオカイ市を対象として空白
ド テイ ハン・米澤剛・ラガワン ベンカテッシュ

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Comparison Machine Learning-based Land Cover Classification Derived
from Sentinel-2 and Landsat-8 Images: A Case Study in Lao Cai, Vietnam
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Do T. HANG, YONEZAWA Go and RAGHAVAN Venkatesh
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大阪市立大学工学研究科, Graduate School of Engineering, Osaka City University,
3-3-138 Sugimoto, Sumiyoshi-ku, Osaka 558-5858, Japan. E-mail: dohanghumg@gmail.com

キーワード：土地被覆，畳み込みニューラルネットワーク，Sentinel-2, Landsat-8
Keywords: Land Cover, LCNN, Sentinel-2, Landsat-8

空1. Introduction
A machine learning model Light Convolutional Neural
Network (LCNN) has been successfully applied on Land
Cover (LC) classification (Song et al., 2019; Do et al.,2020). Freely accessible Remote Sensing (RS) data suchas Sentinel-2 and Landsat-8 has been widely used formapping LC.
The multispectral Sentinel-2 is launched in 2015 which
provides 13 spectral bands. Landsat-8 launched in 2013
includes 11 spectral bands. The two images are useful for
agriculture, forestry and environmental monitoring. This
study aims to compare the capability of the two RS data
on LC classification for Lao Cai area in Vietnam.

2. Data and Methodology
2.1. Study area and data
The study area covering an area of approximately 525
km2 is located in Lao Cai province in the North ofVietnam. Seven main LC categories of the area which are
Water (W), Built-up (B), Mining/Bare land (MB), Rice
Terrace (RT), Paddy Field (PF), Non-Forest Vegetation(NFV) and Forest (F) are classified in this research.
In this study, 4 bands at 10 m resolution (blue, green,red, NIR) of Sentinel-2 image captured on 3rd November
2018 and 6 bands at 30 m spatial resolution (blue, green,red, NIR, SWIR 1, SWIR 2) of Landsat-8 image obtained
on 9th October 2014 covering the study area aredistinguished for LC mapping. Reference polygon
samples of the seven LC classes of the area are collectedbased on referring the samples used in Do et al. (2020)and visual interpretation of Sentinel-2 images withverification using Google Map.
The LCNN model employed in this study is as same asin Do et al. (2020). It includes 3 convolutional layers: Thefirst layer with 20 filters of size 3 × 3 × n, where n is
number of input bands of the RS data, the second andthird layers have 20 filters of size 2 × 2 × 20. A Softmaxlayer is used for providing a probability distribution over
7 LC classes. Zero padding and stride equals 1 areemployed. ReLU activation function and Adam optimizer
with a learning rate of 10-5 are used. The number ofepochs equals to 100, early stopping is applied. TheLCNN model is implemented using Google Colaboratory
framework. Finally, the classified LC maps extracted
from the two RS data are evaluated and compared.
Comparison of training and validation for Sentinel-2
indicates drastic decreases in the first 10 epochs (Figure1 (a)) and subsequently shows a slow decrease andremains steady value after 100 epochs. The loss forvalidation set fluctuates above loss for training set.
Accuracy of training set increases in the first 10 epochs
and subsequently almost stable. On the other hand,accuracy of validation set fluctuates below the values oftraining set (Figure 1 (b)). In case of Landsat-8, loss andaccuracy for training and validation data are the similar
to Sentinel-2 (Figure 1 (c) and (d)) but very little
fluctuations observed for validation data is much less.
The model terminates after 100 epochs. It is suggested
that the model produces reliable results after 100 epochs.

3. Results
Figures 3(a) and 3(b) show the classified maps using
Sentinel-2 and Landsat-8, respectively. Landsat-8
produces higher User’s Accuracy (UA), Producer’s
Accuracy (PA) and Overall Accuracy (OA) for all LCclasses, except PA value of water class (Figure 2). WhileSentinel-2 image attains total OA value of 91%,Landsat-8 shows higher OA value of 97%.
OA of all LC classes of Landsat-8 are higher than 92%.
RT and F are classified at the highest OA, at 98%. In
contract, the OA values of Sentinel-2 are lower: NFVclass is observed at the lowest accuracy, at 83%, while thehighest accuracy is only 94% for B and F.
In general, higher resolution RS image could provide
better LC classification result than lower resolution data.
However, Korhonen et al. (2017) reported that theestimation accuracy of Landsat-8 is similar to Sentinel-2(2017). This study also confirms that Landsat-8 image
achieves better LC classification than Sentinel-2.

10(a)

21.5
Loss10.502040

Epoch6080100(a)10080

Accuracy (%)

(b)

060
4020(c)in Built-up class, while at coarser resolution of 30 m
Landsat-8 image, the sub-classes are shown at the same
spectral detail. As a result, Landsat-8 provides betterclassification result over Sentinel-2.
The obtained results suggest that Landast-8 image ismore useful than Sentinel-2 on LC classification.
Moreover, not only spatial resolution of RS images, but
spectral bands selected and the level of details of LCclasses could be important factors affect to LC
classification result.

020400204002040

Epoch6080

1006080

1006080

100

1.6

Loss

1.2
0.8
0.40(b)100
Accuracy (%)(d)

Epoch80
604020Training

Epoch

Validation

Figure 1: (a) Loss (b) Accuracy for Sentinel-2; (c) Loss (d)Accuracy for Landsat-8

Table 1. Classification accuracy
LC class
Water
Built-up
M/B
Paddy Field
Rice Terrace
NFVForestTotal OA

Accuracy (%)
Sentinel-2Landsat-8PAUAOAPAUAOA
98
76
86
94
97
95
97
91
94
98
92
95
82
89
85
94
98
96
8191
86
93
96
95
90
94
92
100
96
98
83
83
83
96
88
92
95
94
94
98
99
98
91
97

UA: User’s Accuracy, PA: Producer’s Accuracy, OA: Overall Accuracy.

The reason for lower classification accuracy of Sentinel-2
could be due to fewer used spectral bands (4 bands) thanLandsat-8 (6 bands). Moreover, the level LC details
recorded at different resolutions could affect theclassification accuracy. For example, Built-up class is thecombination of road, building and factory sub-classes
which are displayed at different spectral information in
10 m resolution Sentinel-2 image, leads to mixed-spectral

W

BM/BPFRTNFVF

Figure 2: LC classification maps (a) Sentinel-2, (b) Landsat-8

Reference
Do, H. T., Raghavan, V. and Yonezawa, Go. (2020). Land
use/land cover classification using Light convolutional
neural network: a case study in Lao Cai, Vietnam.
GeoInformatics, vol. 31, pp. 19-20.
Korhonen, L., Hadi, Packalen, P. and Rautiainen M.
(2017). Comparison of Sentinel-2 and Landsat 8 in theestimation of boreal forest canopy cover and leaf areaindex. Remote Sensing of Environment, vol. 195, pp.
259-274.
Song, H.; Kim, Y. and Kim, Y. A. (2019) Patch-BasedLight Convolutional Neural Network for Land-CoverMapping
Using
Landsat-8
Images. RemoteSensing. no. 11, 114.