第31回日本情報地質学会講演会 講演要旨集 19-20頁 2020年

Land Use/Land Cover Classification using Light Convolution Neural
Network: a Case Study in Lao Cai, Vietnam
Hang T. DO*, Venkatesh RAGHAVAN** and Go YONEZAWA****Graduate School for Creative Cities, Osaka City University, 3-3-138 Sugimoto, Sumiyoshi-ku, Osaka 558-5858, Japan**, *** Graduate School of Engineering, Osaka City University, 3-3-138 Sugimoto, Sumiyoshi-ku, Osaka 558-5858,
Japan
Email: *dohanghumg@gmail.com, ** raghavan@osaka-cu.jp , *** yonezawa@osaka-cu.jp

Key words: LULC, LCNN, Remote sensing, RapidEye, Object-based

1. Introduction
Convolution Neural Network (CNN) is currently thestate of the art for many remote sensing
image classification tasks. By learning with multiple
hidden layers, CNN models have shown outstanding
accuracy in various applications (Masi et al., 2017, Rezaee
et al., 2018). Traditional CNN models which include
numerous convolutional layers are very time-consuming
and require a huge training data and computationalresources. In comparison, Light CNN (LCNN) with only
few convolutional layers can achieve high accuracy in ashort processing time by using a small number of training
samples (Song et al., 2019). In this study, pixel-based andobject-based LCNN is applied to establish Land Use/
Land Cover (LULC) map of Lao Cai area in Vietnam.

2. Methodology
2.1. Data and study area
The study area, a part of Lao Cai province, located in
the North of Vietnam covers an area of approximately 525
km2. The study area is the settlement area for ethnic
minorities. The main LULC classes are water, built-up,
mining/bare land, rice terrace, paddy field, non-forest
vegetation and forest.
RapidEye's sensors produce imagery in five spectral
bands at 5m resolution. In this study, 5 bands of RapidEye
image acquired on 9th September 2014 covering the study
area are used for LULC extraction. Reference polygon
samples of the 7 LULC classes were collected based on
visual interpretation of RapidEye image with verification
using Google Map (Figure 1). The reference samples cover
82.29 km2, which equals 14.9% study area.
2.2. Methodology
In CNN model, convolutional layer is the first layer
to extract feature maps from an input image. Inconvolution, the filter moves through the entire input
image with the moving step decided by setting stride
hyperparameter. If the stride is 1, the filter is moved to 1
pixel at a time. Sometimes the filter does not perfectly fit
the input image size. Zero-padding is to pad the input
image with zeros so that the filter will fit the image.
Pooling layers are used to progressively reduce thenumber of parameters when the images are too large. In
fully connected layer, the feature maps extracted fromprevious layer are flattened into one-dimensional vector
and fed to a fully connected network. While theconvolutional layers learn the spatial features, the fully
connected layers learn the classification rule to extract
feature vectors by using an activation function. Theoutput of full connected layer is determined by using anactivation function.
2.2.1. PBIA and OBIA convolutional layer
Basically, in first convolutional layer, large filter sizes
could obtain better classification result than smaller filter
sizes considering the relation of a number of neighborpixels of input image. In this case, it is considered as
patch-based approach, or OBIA. However, when largefilter sizes are used, such as 5×5 and 7×7, the number ofparameters and computation cost increase drastically. To
resolve the problem, a 1×1 filter which generates only asingle parameter or weight for each channel of the input
image, and small filter size 3x3 were employed (Lin, et al.,2013). The 1×1 filter does not involve any neighbor pixels
in the input but works with the individual pixel itself. In
this case, the convolutional layer could be considered asPBIA. However, the layer needed to be combined anonlinearity with other convolutional layers, allowing theprojection to perform non-trivial computation on the input
feature maps. In this study, CNN model with PBIA
convolutional layer is considered as PBIA-CNN, while themodel with OBIA convolutional layer is named OBIACNN.

Water
Built-up
Mining/Bare land
Paddy field
Rice terrace
Non-forest vegetation
ForestFig 1. Reference map of this study

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2.2.2. LCNN and OBIA-LCNN
In CNN model, when the number of network layers
increase, the information in the neurons of the network is
continuously combined. Eventually, the network extracts
deep concepts and expresses abstract semantic features(Song et al., 2019). However,a deep network can lead totime-consuming and overfitting. To ignore thesedrawbacks, LCNN is applied for LULC classification inthis study. The workflow of PBIA-LCNN and OBIA-LCNN
are displayed in Figure 2. The two models have 3
convolutional layers. The first convolutional layer ofPBIA-LCNN filters the 3D input with 20 filters of size
1×1×5. In case of OBIA-LCNN, the first convolutional
layer includes 20 filters of size 3×3×5. The second andthird convolutional layers of PBIA-LCNN and OBIALCNN have 20 filters of size 2×2×20. Zero padding andstride equals 1 are employed. The last Softmax layer
which provides a probability distribution over 7 LULC
classes. Fully connected layers and pooling layers are notemployed. Instead, ReLU activation function and Adam
optimizer with a learning rate of 10-5 are used. The
number of epochs equals to 100 and early stopping
technique is applied, the training processing stops when
the different between two consecutive loss (r) is lower orequals to 10-6. Google Colaboratory framework is selected
to implement the models in this study.
(a)Conv 1

Conv 2

Conv 3Soft-3. Result and Conclusions
Figure 3 shows the result LULC maps of PBIALCNN and OBIA-LCNN using RapidEye image. Table 4.1
gives producer’s, user’s and overall accuracy of theclassification.
In PBIA, all LULC maps extracted from RapidEye
image produce high overall accuracies (Table 1). Amongthem, water, rice terrace and forest achieve excellent
accuracies, at 96%, 95% and 97%, respectively.
Mining/bare land, built-up, paddy field and non-forest
vegetation attain lower accuracies, at 89%, 92%, 88% and89%, correspondingly. The difference between producer’s
accuracies and user’s accuracies of all classes are small,less than or equal to 5%. Overall accuracy of theclassification is 94%.
Classification accuracies of all LULC classes in OBIA
are more than 87%. Similar to the PBIA, water, riceterrace and forest are the most accurate classes, at 97%,95%, 97%, respectively, followed by mining/bare land,built-up, paddy field and non-forest vegetation, at 91%,93%, 87% and 90%, respectively. In general, the difference
between producer’s and user’s accuracies of individual
LULC classes are small, no more than 5%. Overall
classification accuracy is equal to PBIA, at 94%.
The result shows that both PBIA-LCNN and OBIALCNN are effective classification techniques for LULC
mapping. Moreover, the LCNN models have capability ofhandling large RS datasets to aid in monitoring LULC
change on a local as well as regional scale using RS
images.

max

(a)

Input
LULC classes(b)
Conv 1

Conv 2

Conv 3

Softmax

Input
LULC classes

Fig 2. Architecture of (a) PBIA-LCNN, (b) OBIA-LCNN
Table 1: Classification accuracy (MB: Mining/bareland,
NF: Non-forest vegetation)
Accuracy (%)
PBIA
OBIA
PAUAOAPAUAOA
Water
97
96
96
98
96
97
Built-up
94
91
92
94
92
93
MB
90
88
89
90
91
91
Paddy field
83
88
88
85
90
87
Rice terrace
95
94
95
97
94
95
NF
90
88
89
89
91
90
Forest
96
97
97
98
96
97
OA
94
94
UA: User’s Accuracy, PA: Producer’s Accuracy, OA: OverallAccuracy
LULC class

References
Lin, Min, Chen, Qiang, and Yan, S. (2013) Network in network.
arXiv preprint arXiv:1312.4400.
Masi, G., Cozzolino, D., Verdoliva, L. and Scarpa, G. (2017)
CNN-based pansharpening of multi-resolution remotesensing images, 2017 Joint Urban Remote Sensing Event(JURSE), pp. 1-4.

Water
Built-up
Mining/Bare land
Paddy field
Rice terrace
Non-forest vegetation
Forest(b)

Fig 3. LULC
classification maps(a) PBIA-LCNN,(b) OBIA-LCNN

Rezaee, M., Mahdianpari, M., Zhang, Y. and Salehi, B. (2018)
Deep convolutional neural network for complex wetlandclassification using optical remote sensing imagery. IEEE

Journal of Selected Topics in Applied Earth Observations
and Remote Sensing, vol. 11, no. 9, pp. 3030-3039.

Song, H.; Kim, Y. and Kim, Y. A. (2019) Patch-Based Light
Convolutional Neural Network for Land-Cover Mapping
Using Landsat-8 Images. Remote Sensing. no. 11, 114.