Journal of Robotics, Networking and Artificial Life
Vol. 8(2); September (2021), pp. 139–144
DOI: https://doi.org/10.2991/jrnal.k.210713.014; ISSN 2405-9021; eISSN 2352-6386
https://www.atlantis-press.com/journals/jrnal

Research Article

Anomaly Detection Using Convolutional Adversarial­Autoencoder and One-class SVM for Landslide Area
Detection from Synthetic Aperture Radar Images
Shingo Mabu1,*, Soichiro Hirata1, Takashi Kuremoto2
Graduate School of Sciences and Technology for Innovation, Yamaguchi University, 2-16-1 Tokiwadai, Ube, Yamaguchi 755-8611, Japan
Department of Information Technology and Media Design, Nippon Institute of Technology, 4-1 Gakuendai, Miyashiro-machi,
Minamisaitama-gun, Saitama 345-8501, Japan
1
2

ARTICLE INFOABSTRACT

Article History

An anomaly detection model using deep learning for detecting disaster-stricken (landslide) areas in synthetic aperture radar
images is proposed. Since it is difficult to obtain a large number of training images, especially disaster area images, withannotations, we design an anomaly detection model that only uses normal area images for the training, where the proposedmodel combines a convolutional adversarial autoencoder, principal component analysis, and one-class support vector machine.
In the experiments, the ability in detecting normal and abnormal areas is evaluated.

Received 25 November 2020
Accepted 23 May 2021
Keywords
Anomaly detection
adversarial autoencoder
one-class SVM
synthetic aperture radar© 2021 The Authors. Published by Atlantis Press International B.V.
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

1. INTRODUCTION
In Japan, typhoons often approach and pass from July to September
every year because of the effects of westerly winds and high atmospheric pressures. Landslide disasters frequently occur due to heavy
rains caused by typhoons, which leads to major accidents related totransportation and human life. Until now, observation of the disaster areas, e.g., landslides, has been mainly conducted by aircraft [1].
However, in recent years, remote sensing using satellite images has
attracted attention as a method for observing a wide area [2]. In satellite remote sensing, a sensor is mounted on an artificial satellite toobserve the ground surface of the earth, and the obtained image isanalyzed. Therefore, we can see the damages without going directly tothe disaster areas. Typical satellite images include optical images thatare obtained from sunlight reflection, and Synthetic Aperture Radar(SAR) images that are obtained by sensors that emit microwaves to theground surface. It is easy for human eyes to interpret optical images,
but they cannot be observed at night or in bad weather. On the other
hand, SAR can observe the surface of the earth regardless of time andweather; thus, SAR images are useful for rapid rescue activities at night
and in bad weather conditions. However, it is difficult for human eyes
to interpret SAR images, unlike optical images. Therefore, manymethods that analyze SAR images have been proposed to detect disaster areas rapidly [3,4], and machine learning techniques, especiallydeep learning, have also been applied to landslide area detection [5].
When deep learning is applied to disaster area detection, a large
number of training samples are necessary. However, shooting
Correponding author. Email: mabu@yamaguchi-u.ac.jp*c­ onditions of SAR images are different time by time, e.g., seasons,and it is also difficult to obtain a sufficient number of training samples with annotation (correct class labels) immediately after a disasteroccurs. To overcome this problem, we propose an anomaly detection system that detects abnormal images that deviate from normal
images. The proposed method uses only normal images for the training without using abnormal (disaster) images. Normal images are relatively easy to obtain compared with abnormal images; thus anomaly
detection is useful in a practical situation of disaster area detection.
The proposed method consists of a feature extraction part and a
detection part. The feature extraction is realized by Convolutional
Adversarial Autoencoder (CAAE) [6,7] and Principal Component
Analysis (PCA). CAAE is an extension of Adversarial Autoencoder(AAE) [8], where the layers of AAE are replaced with convolution
and deconvolution layers. CAAE is trained with unsupervised learning; thus the cost of the annotation is not necessary. The detection
part is realized by One-Class Support Vector Machine (OCSVM)[9–11] that detects outliers that deviate from the normal region.
This paper is organized as follows. In Section 2, the mechanism andfeatures of SAR are introduced. In Section 3, the proposed methodthat combines CAAE, PCA, and OCSVM is explained. In Section 4,
the experimental conditions and results are described. Finally,
Section 5 is devoted to conclusion.

2. SYNTHETIC APERTURE RADAR
Synthetic aperture radar is an active image radar that synthesizes
small antennas mounted on a platform, such as an aircraft or satellite,
to realize large virtual antennas and generates high-resolution radar

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images [12–14]. Because SAR is an active sensor that emits microwaves, it is possible to observe the surface of the earth ­regardlessof the presence or absence of sunlight and clouds. SAR images areapplied to the research fields of agriculture, disaster, oceans, earthscience, and so on [15,16]. SAR emits microwaves and receives thereflected microwaves from the ground surface (Figure 1). When amicrowave emitted from the SAR antenna enters a conductor ordielectric, a current is induced, and the microwave is reemitted
from the induced current. This is called scattering and scattering in
the opposite direction of the incident wave is called backscattering.
Because backscattering is the diffuse reflection caused by scattering, backscattering is different from specular reflection.
Synthetic aperture radar receives the backscattering and executes
image reproduction. The scattering intensity of the microwaves
strongly depends on the frequency, wavelength, and electric characteristics (dielectric constant, etc.) of the scatterer. Therefore, forexample, seawater or cars made of metal strongly reflect microwaves because a current is easily induced. On the other hand,sand and trees have low reflectivity because hardly any current is
induced. In each pixel of SAR images, the intensity of the received
microwave is recorded. A SAR image used in this paper was taken
by Advanced Land Observing Satellite No. 2 (ALOS-2). ALOS-2
is used in a variety of fields such as disaster monitoring, observation of forest distribution, and crustal movement measurement.
In particular, ALOS-2 can detect changes in the distance from thesatellite to the ground of the earth with an accuracy of centimeters,
which contributes to detailed understandings of earthquakes, volcanic activities, and heavy rains. The main purpose of ALOS-2 is toensure the safety of our daily lives and to solve global environmental problems [17].

3. MATERIALS AND METHODS
3.1. Dataset
A SAR image used in this paper is shown in Figure 2. It shows
the northern Kyushu area in Japan on July 7, 2017, where largescale landslides, due to the torrential rain from July 5 to 6, 2017,
occurred in this area. The red areas in Figure 2 shows the locationsof the landslide that were annotated based on the information provided by the Geospatial Information Authority of Japan [18]. The
size of the image is 6648 × 4360 [pixels] and the resolution per pixel
is about 3 m2.

Figure 2 | SAR image and landslide areas (red areas).

Figure 3 | Examples of 128 × 128 [pixels] ROI images.

Figure 4 | ROI and a target area for detection.

In this paper, Region of Interest (ROI)-based anomaly detection
was carried out, that is, we first divided the whole SAR image into
128 × 128 [pixels] ROI images. Figure 3 shows examples of normal
and abnormal ROI images. Note that the target area for the detection of normal and abnormal is the central 32 × 32 [pixels] area of
each ROI as shown in Figure 4. The surrounding area of the target
area is used as a piece of supporting information (context) for thedetection. The ROI images were split into training data including
normal only, testing data of normal, and testing data of abnormal.

3.2. Method
The overview of the anomaly detection model designed in this paper
is shown in Figure 5. The feature extraction from SAR images are
realized by CAAE that transforms input image x to feature vector z.
Then, z is transformed to z¢ by PCA for dimensionality reduction,and finally OCSVM classifies z¢ as normal or abnormal.

3.2.1. Feature extraction using ­convolutional
adversarial autoencoder
Figure 1 | Microwave emission, scattering, and backscattering of SAR [14].

In this paper, the extended model of AAE, named CAAE, is used
for the feature extraction. Figure 6 shows the detailed structure of

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141

CAAE that extracts feature vectors from ROI images. The upper
part of Figure 6 is a CAE that contains an encoder and a decoder.
The encoder outputs a 128-dimensional feature vector z, and thedecoder outputs a reconstructed ROI image. The encoder consists of some Block1 and fully-connected layers, where a Block1
contains convolution and max-pooling layers along with batchnormalization [19] and Rectified Linear Unit (ReLU) [20]. The
decoder consists of some Block2, fully-connected and deconvolution layers, where a Block2 contains a deconvolution layer alongwith batch normalization and ReLU. The loss function (LOSS1 in
Figure 6) is the mean squared error between the input and output
images, and the weights of the encoder and decoder are updated
by error backpropagation with Adam [21] to minimize LOSS1. As
the learning of minimizing LOSS1 proceeds, the images generated
by the decoder gradually match the input images and feature z cancontain important information that is necessary to reconstruct the
input images. The encoder and discriminator at the lower part of
Figure 6 are adversarial networks, where the discriminator distinguishes vectors generated by Gaussian distribution (regarded
as positive or real samples) zr from feature vector z inputted fromthe encoder (regarded as negative or fake samples). The loss function (LOSS2 in Figure 6) is the softmax cross-entropy based on thepositive and negative probabilities outputted by the discriminator.
The discriminator updates the weights to minimize LOSS2, while
the encoder updates the weights to maximize LOSS2. Here, we
explain the meaning of learning with LOSS2. Maximizing LOSS2
means that the encoder aims to cheat the discriminator so that the­ iscriminator misclassifies the negative samples as positive samd
ples. On the other hand, minimizing LOSS2 means that the discriminator aims to correctly classify positive and negative samples.
As a result of the above adversarial learning, the distribution of feature vector z gradually follows Gaussian distribution. This adversarial learning is a kind of regularization to avoid overfitting by
imposing a constraint on the feature distribution.

Figure 5 | Overview of the proposed method.

Figure 7 | Effects of ν in OCSVM.

3.3. Dimensionality Reduction by PCA
and Anomaly Detection by One-Class­Support Vector Machine
The feature vector z generated by CAAE is transformed to z¢ by
PCA. PCA executes a linear mapping of feature vectors to a lowdimensional space, where the variance of the vectors in the lowdimensional space is maximized. In this paper, 128 dimensions of z
are reduced to 40 dimensions by applying PCA. Then, z¢ is inputtedto OCSVM. SVM is originally a supervised-learning-based classification algorithm for two classes, which needs training data withclass labels. OCSVM needs only one-class data for the training,where it generates areas of the trained class in the feature space.
After the training, OCSVM regards the data that deviates from theareas of the trained class as outliers, i.e., abnormal. OCSVM has aparameter ν that determines the proportion of normal and abnormal areas. For example, when ν is set at a large value as shown in
Figure 7 (left), the normal area becomes small. In this case, only thedata that is very near to the center of the normal area is regarded

Figure 6 | The detailed structure of CAAE.

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142

as normal, which increases the sensitivity of abnormal but may
increase false positives, in other words, the true negative (normal)
is misclassified as positive (abnormal). Conversely, when ν is small,the normal area becomes large, which increases the specificity,
but may increase false negatives, in other words, the true positive(abnormal) is regarded as negative (normal). Therefore, the value ofν should be determined carefully considering the trade-off between
false positive and false negative.

4. EXPERIMENTS
4.1. Condition 1
The class labels of ROIs were given by the following rule. When
60% or more area of the central 32 × 32 area of each ROI contains the abnormal area, it is regarded as abnormal, otherwise,
normal. The number of training data (normal only), that of testingdata (normal), and that of testing data (abnormal) are shown in
Table 1. CAAE was trained on the training data and all the training
data were encoded as feature vectors. Then, PCA was applied to thefeature vectors to reduce the number of dimensions from 128 to
40. The feature vectors after the dimensionality reduction are used
to train OCSVM. Finally, the testing images encoded and transformed by the trained CAAE and PCA were classified as normal or
abnormal by OCSVM.
Table 2 shows accuracy, sensitivity, specificity, and precisionobtained by the combination of CAE, PCA, and OCSVM (called
the conventional method), and Table 3 shows those obtained by
the combination of CAAE, PCA, and OCSVM (called the proposed
Table 1 | The numbers of ROI images in condition 1
Training/Testing

The number of images

Training (normal only)
Testing (normal)
Testing (abnormal)

13,542
164
164

Table 2 | Results obtained by CAE + PCA + OCSVM (Conventional
method) in condition 1νAccuracy
Evaluation
Sensitivity
metric (%) Specificity
Precision

method). The best accuracy obtained by the conventional method
is 55.7% (ν = 0.5) and that obtained by the proposed method is57.9% (ν = 0.4). Therefore, we can see that CAAE is better thanCAE as a feature extraction method; however, there is a large
room for improvement on the accuracy. Regarding the sensitivity,
specificity, and precision, the conventional method shows higher
sensitivity than the proposed method when ν is large, while theproposed method shows higher specificity. Although the conventional method can find more abnormal data than the proposed
method, the precision of the proposed method for all the settingsof ν is better than the conventional method. Therefore, we can saythat the proposed method makes positive predictions when it has
high confidence, which results in smaller false positives.

4.2. Condition 2
In condition 2, the class labels of ROIs were given by the following
rule. The rule of abnormal areas is the same as condition 1, that is,when 60% or more area of each ROI contains abnormal, it is regarded
as abnormal. In the case of the labeling of normal, ROIs without containing any abnormal areas are regarded as normal. In condition 1,
the threshold that separates the normal and abnormal ROIs was 60%,
which means that even if the ROIs contain 59% abnormal areas, theyshould be classified as normal. To clearly separate the features ofnormal and abnormal, condition 2 adopted the above labeling rule.
Table 4 shows the numbers of training and testing data. The procedureof executing CAAE, PCA, and OCSVM is the same as condition 1.
Table 5 shows the accuracy, sensitivity, specificity, and precision of
the conventional method and Table 6 shows those of the proposed
method. The best accuracy obtained by the conventional method
is 61.4% (ν = 0.2) and that obtained by the proposed method is
66.0% (ν = 0.5); thus we can see that the accuracy becomes better
than condition 1 and also the accuracy obtained by the proposed
method is better than that by the conventional method. There isa similar tendency in the sensitivity, specificity, and precision as
in condition 1. That is, the sensitivity of the conventional method
is higher than the proposed method, while the proposed method
shows higher specificity and precision. Therefore, the reliability ofthe positive prediction of the proposed method is better than the
Table 4 | The numbers of ROI images in condition 2

0.2

0.3

0.4

0.5

0.6

0.7

53.4
23.4
83.4
58.5

54.9
32.1
77.8
59.1

55.6
41.1
70.1
57.9

55.7
51.1
60.3
56.3

49.4
56.1
42.7
49.5

47.4
63.8
31.1
48.1

Table 3 | Results obtained by CAAE + PCA + OCSVM (Proposed
method) in condition 1

Training/Testing

The number of images

Training (normal only)
Testing (normal)
Testing (abnormal)

7967
164
164

Table 5 | Result obtained by CAE + PCA + OCSVM (Conventional
method) in condition 2

νAccuracy
Evaluation
Sensitivity
metric (%) Specificity
Precision

ν

0.2

0.3

0.4

0.5

0.6

0.7

55.9
24.5
87.3
65.9

56.6
31.5
81.6
63.1

57.9
37.1
78.7
63.5

57.4
39.4
75.4
61.6

54.4
41.9
66.9
55.9

52.4
47.2
57.6
52.7

Accuracy
Evaluation Sensitivity
metric (%) Specificity
Precision

0.2

0.3

0.4

0.5

0.6

0.7

61.4
39.5
83.3
70.3

60.4
47.7
73.1
63.9

61.2
56.6
65.8
62.3

59.8
64.3
55.5
59.1

58.7
68.1
49.3
57.3

56.2
73.3
39.1
54.6

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Table 6 | Result obtained by CAAE + PCA + OCSVM (Proposed method)in condition 2
νAccuracy
Evaluation Sensitivity
metric (%) Specificity
Precision

0.2

0.3

0.4

0.5

0.6

0.7

64.0
39.6
88.3
77.2

64.8
44.4
85.1
74.9

65.5
50.0
80.9
72.4

66.0
54.8
77.2
70.6

62.7
56.9
68.5
64.4

59.6
60.6
58.5
59.4

conventional method. However, we should improve the sensitivity
in the future work to find the abnormal areas as many as possible.

5. CONCLUSION
In this paper, we proposed an anomaly detection method to classify normal (non-disaster) areas and abnormal (disaster/landslide) areas, where the proposed method consists of CAAE, PCA,
and OCSVM. In the experiments, we evaluated the detection performance in the two conditions. From the results, it was clarified
that CAAE is better than CAE as a feature extraction method.
The remaining problem is the classification of ROI images that
contain small abnormal areas. Since it is difficult to make a sharpboundary between normal and abnormal, other methods suchas segmentation models would be useful. Also, the proposed
method should be evaluated using other SAR images to confirm
the detection ability.

CONFLICTS OF INTEREST
The authors declare they have no conflicts of interest.

ACKNOWLEDGMENT
This work was supported by JSPS KAKENHI Grant Number
JP20H02417.

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AUTHORS INTRODUCTION
Dr. Shingo Mabu

Dr. Takashi Kuremoto

He received his B.E. and M.E. degrees
from Kyushu University, Japan, in 2001
and 2003, respectively, and his PhD
degree from Waseda University, Japan,
in 2006. From 2006 to 2007, he was a visiting lecturer at Waseda University, andfrom 2007 to 2012, he was an AssistantProfessor at the same university. From2012 to 2017, he was an AssistantProfessor at Yamaguchi University, Japan, and from 2017 to2020, he was an Associate Professor at the same university. Since
2020, he has been a Professor at the same university.

He received his B.E. degree from the
University of Shanghai for Science andTechnology, China, in 1986 and his
M.E. and PhD degrees from Yamaguchi
University, Japan, in 1996 and 2014,
respectively. He worked as a system engineer at the Research Institute of Automatic
Machine of Beijing from 1986 to 1992,
and until 2021, he was an AssistantProfessor at Yamaguchi University. He was an Academic
Visitor of the School of Computer Science, The University ofManchester, U.K., in 2008. Currently, he is a Full Professor with
the Department of Information Technology and Media Design,
Nippon Institute of Technology, and visiting researcher of The
University of Tokyo. His research interests include artificial
neural networks, fuzzy, evolutionary computation, time series
forecasting, and intelligent systems.

Mr. Soichiro Hirata
He received his B.E. and M.E. degrees
from Yamaguchi University, Japan in 2018and 2020, respectively. He is currently
working at NEC Corporation.