第29回日本情報地質学会講演会 講演要旨集 027-028頁 2018年

Pixel- and Object-based Deep Neural Network LULC Classification using
Remote Sensing Data and R Software
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
Email: *dohanghumg@gmail.com, ** raghavan@media.osaka-cu.jp , *** yonezawa@media.osaka-cu.jp

Key words: LULC, Deep learning, Object-based, R-keras, SGD

1. Introduction
Deep-learning (DL) algorithms, which learn therepresentative and discriminative features in ahierarchical manner from the data have been applied for
RS data analysis, including Land Use/ Land Cover(LULC) classification (Zhang L. et al., 2016). R is the free
software environment for statistical computing andgraphics which provides Keras package - a high levelneural networks API develop which a focus on enabling
fast experimentation. In this study, a deep learningalgorithm in Keras named Stochastic gradient descent(SGD) optimizer is explored to establish LULC map frommultispectral RapidEye imagery in pixel-based andobject-based approaches.

2. Methodology
2.1. Data and study area
Remotely sensed data used is 5 meters RapidEye
multispectral dataset acquired on September 2014
covering a part of Lao Cai province, Vietnam. Main
LULC classes: water, bare land, road, building, paddy
field, terrace field and forest will be classified. Reference
data is collected from forest map in scale 1:10000(Ministry of Agriculture & Rural Development, Vietnam).
144569 points of reference data (number point of each
LULC classes is selected based on square area of theclass) is separated to three sets: training set (80%);validation set (13%) and test set (7%).
2.2. Methodology
2.2.1. Segmentation
Image segmentation technique is a process ofgrouping similar neighbor pixels to objects. To assess thedynamics of local variance (LV) from an object level toanother, Rate of Change of Local Variance (ROC)
measurement is used:

Where L is LV value (defined by standard deviation
value of pixels inside segment of target level and (L-1) isthe value of next lower level. Peaks in the ROC graphshow the object levels at which the segments match thetypes of objects characterized by equal degrees ofhomogeneity (Draguţ, L. et al., 2010). Segments atthreshold 0.03, 0.14, 0.23, 0.26 and 0.45 are used for deeplearning classification (Fig 1).

Fig 1. ROC graph, segments at threshold 0.03, 0.14, 0.23,0.26 and 0.45 are used for next analysis step.
2.2.2. SGD
SGD is a stochastic approximation of the gradientdescent optimization and interactive method forminimizing and objective function (Botou, L., 1998). In a
simple supervised learning setup, each example z is apair (x, y) where x is arbitrary input and y is scalar
output. The empirical risk En(f) measures theperformance of training set is calculate:

Where l is loss function, f w(x) is the function
parameterized by a weight vector w, f is the function
which is seek to minimizes the loss l(fw(x),y), dP(z) is
unknown distribution that embodies the Laws of Nature.
The expected risk E(f) determines the generalization
concert. SGD algorithm is the drastic simplification
which is not computing the gradient of En(fw) precisely,
each iteration estimates this gradient based on a singlerandomly selected example zt:
The stochastic process {wt, t = 1 …} depends on theselected random examples at each interaction. Since thealgorithm does not need to remember which examples
were visited through the previous iterations, it cananalyze examples on the fly in a deployed system. It
leads SGD can optimize the expected risk directly, since
the examples are randomly drawn from the reference
distribution (Bottou, L., 2010).

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2.2.3. SGD parameters setting up
Deep neural network SGD uses five bands ofRapidEye image as input of network; output is the seven
LULC classes. 5 bands input and reference data areconverted to ascii format. Main parameters of thenetwork are established: hidden layers = 4, neurons =150, l1 = l2 = 0.00001, learning rate = 0.01, activation of
input and hidden layers is rectified linear unit („relu‟), of
output layer is softmax normalization („softmax‟). The
model named “keras_model_sequential”, Stochastic
gradient descent optimizer is defined with learning rate
= 0.01. The model then is compiled using „sparse_categorical_crossentropy‟ loss function and training set. Next,
the model is fitted to the validation data using callbacks
function named “callback_tensorboard”. R script is
modified based on Github (Zia Ahmed, 2018). Since thetraining process finishes, test set then uses for assessing
accuracy of the classification method. The neuralnetwork is shown in Fig 2.

and forest (97.7%) correspondingly. Especially, roadshows the lowest accuracy value in most of approachcases in comparison with other classes. Road, building
and terrace field classes accuracies various in larger
range than other classes.
In future work, combination of pixel-based andobject-based approach may be established as a more
effective classification solution. On the other hand, it
needed to find a method that can support to selectoptimal parameters from a lot of available parameters ofSGD optimizer.

Pixel-based

Object-based, threshold = 0.03

Fig. 2. Deep neuron network model of this study

3. Result and Conclusions

Object-based, threshold = 0.14 Object-based, threshold = 0.23

LULC maps in pixel-base and object-base areproduced (Fig. 3). Table 1 shows accuracies of six
classifier maps. The approaches demonstrate very goodclassification results since accuracy values are almost
equal and range from 94.5% to 94.9%. However, eachmethod can classify effectively different LULC classes.
Pixel-based approach illustrates the highest accuracy ofwater (99.6%), building (90.9%) and paddy field (85.9%),
while object-based approaches at threshold=0.03, 0.14,0.23, 0.26 and 0.45 confirm the best outcomes of terrace(97.1%), forest (97.7%), road (84.1%), bare land (91.2%)

Object-based, threshold = 0.26 Object-based, threshold = 0.45
Fig 3. LULC classification maps

Table 1. Accuracy of Pixel-based and Object-based LULC classification (%)

Objectbased
Pixel-based

Threshold

Water

0.03
0.14
0.23
0.26
0.45

99
99.4
98.4
99.2
99.5
99.6

Bareland
90.9
89.7
90
91.2
89.6
88.5

Road
81.3
78.4
84.1
83.4
77.1
79.6

References
Zhang, Li., Zhang, Le., Du, B. (2016), Deep Learning for
remote sensing data. IEEE Geosciences and remotesensing magazine, 0274-6638/16.
Bottou L. (1998), Online Algorithms and StochasticApproximations. Cambridge University Press, ISBN
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Bottou, L. (2010), Large-scale machine learning with
Stochastic
gradient
descent.
Proceedings
ofCOMPSTAT‟2010, Pyhisca-Verlag,178-179p.

Paddy
Terrace
ForestOverall
field
field
86.7
82.9
97.1
97.6
94.6
89.6
84
95.7
97.7
94.8
88.4
83.9
96
97.4
94.6
87.3
84.8
96.9
97.5
94.8
90.1
83.5
96.5
97.7
94.9
90.9
85.9
96.2
97
94.5
Zia A. (2018), Deep Neural Network with keras-R(TensorFlow GPU backend): Satellite-image classification.
https://github.com/zia207/Satellite-Images-Classificat
ion-with-Keras-R, retrieved on 25th May 2018.
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and performance. New York: Wiley, 144p.
Dragut, L., Csillik, Tiede, D., Levick, S. R. (2010), ESP: a tool to estimate
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