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Local similarity measure for landslide detection and identification incomparison with the image differencing methodSiti Khairunniza-Bejoa; Maria Petroub; Athanassios Ganasc

a Department of Biological and Agricultural Engineering, Faculty of Engineering, Universiti PutraMalaysia, Serdang, Selangor, Malaysia b Department of Electrical and Electronic Engineering, ImperialCollege, London, UK c Geodynamics Institute, National Observatory of Athens, Athens, Greece

Online publication date: 10 December 2010

To cite this Article Khairunniza-Bejo, Siti , Petrou, Maria and Ganas, Athanassios(2010) 'Local similarity measure forlandslide detection and identification in comparison with the image differencing method', International Journal ofRemote Sensing, 31: 23, 6033 — 6045To link to this Article: DOI: 10.1080/01431160903376365URL: http://dx.doi.org/10.1080/01431160903376365

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Local similarity measure for landslide detection and identification incomparison with the image differencing method

SITI KHAIRUNNIZA-BEJO*†, MARIA PETROU‡ and

ATHANASSIOS GANAS§

†Department of Biological and Agricultural Engineering, Faculty of Engineering,

Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia

‡Department of Electrical and Electronic Engineering, Imperial College, London, SW7

2AZ, UK

§Geodynamics Institute, National Observatory of Athens, 118 10 Lofos Nymfon, PO Box

20048, Athens, Greece

(Received 26 February 2008; in final form 18 December 2008)

In this article, a new simple method of landslide detection and identification is

proposed. It is based on the use of local mutual information and image threshold-

ing. A binary change image is then produced. Connected component analysis is

used to identify the connected regions. Landslides are identified as the largest

connected regions in this image. Mathematical morphology is used to approximate

the landslide region. This method is simple and suitable for the detection of large

changed regions where the ratio of the unchanged to changed pixels in the image is

approximately one to a few tens. Compared to the image differencing method, this

method gives more reliable results.

1. Introduction

A landslide is defined as the movement of a mass of rock, debris or earth down a slope.

Earthquakes, heavy rains, volcanic eruptions and erosion by rivers may cause land-

slides. These geological and geographical factors can cause landslides to occur at the

same area as in the past. Under the assumption that future landslides will occur under

conditions similar to those in the past, the area where a landslide occurs is categorized

as belonging to a prone-to-landslide environment. Therefore, an effective way to

prevent future damage is to identify landslide areas. When such an area is identified,its activity may be monitored and specific analysis can be done so that precautions can

be taken.

Remote sensing data are primary sources extensively used for change detection and

identification. There are three important aspects in identifying landslides: size of the

features, their contrast (the difference in spectral characteristics between the land-

slides and their surrounding areas) and their morphological (shape) expression. Many

change detection and identification techniques have been developed. These include

image enhancement (Ostir et al. 2003, Nichol and Wong 2005a, 2005b, Whitworthet al. 2005) image differencing (Hervas et al. 2003, Cheng et al. 2004, Rosin and

Hervas 2005), vegetation index differencing (Lin et al. 2005), image classification

*Corresponding author. Email: [email protected]

International Journal of Remote SensingISSN 0143-1161 print/ISSN 1366-5901 online # 2010 Taylor & Francis

http://www.tandf.co.uk/journalsDOI: 10.1080/01431160903376365

International Journal of Remote Sensing

Vol. 31, No. 23, 10 December 2010, 6033–6045

Downloaded By: [HEAL-Link Consortium] At: 14:52 9 March 2011

(Nichol and Wong 2005a, 2005b) and image registration (Yamaguchi et al. 2003). All

these techniques have their advantages and disadvantages. Image enhancement can be

used to salienate landslides in Earth observation images. However, it requires human

experience and knowledge of the study area for visual interpretation. Image differen-

cing has the advantage of being a simple, straightforward method with results that areeasy to interpret, but it cannot provide a detailed change matrix and it requires the

selection of thresholds. Vegetation index differencing emphasizes differences in spec-

tral response of different features and reduces impacts of topographic effects and

illumination. However, this technique also enhances random noise or coherent noise.

Image classification minimizes the impact of atmospheric, sensor and environmental

differences between multi-temporal images, but it requires the selection of sufficient

training sample data for classification. Image registration can be used to detect land-

slide movements with sub-pixel accuracy, but it has high computational cost in termsof CPU time.

In this article, a new simple method of change detection and identification based on

the use of a local similarity measure is introduced. Mutual information is used as the

measure of similarity. The method is validated by experiments on three landslide

events. It will be shown that the local similarity measure method is significantly better

than the image differencing method. The method can detect landslides which fail to be

detected by using the image differencing method. Compared to the visual change

detection method, this method is semi-automatic and does not require experts tointerpret the data. Therefore, it may reduce the great demand of time and labour.

2. Study area and imagery used

Images of landslide areas taken before and after landslide events in Japan, Indonesia

and Pakistan were used for the experiments. The landslides in Niigata, Japan were

triggered by a string of earthquakes with magnitude ranging from 5.9 to 6.8. The

data used are IKONOS images taken before and after the earthquakes as shown in

figures 1(a) and 1(b), respectively. The image after the earthquake shows one such

landslide on the Enoki Tunnel with mud, rock and trees covering the road along the

river. The landslides in Aceh, Indonesia, were triggered by an underwater earthquakeand tsunamis. Figures 1(c) and 1(d) show a small area along the Sumatran coast in

the Aceh province before and after the tsunami, respectively. The images are taken

from Landsat 7 Enhanced Thematic Mapper Plus (ETMþ). The landslides in

Muzaffarabad, Pakistan, were triggered by a powerful 7.6 magnitude earthquake.

The images before and after the earthquake taken by the Advanced Spaceborne

Thermal Emission and Reflection Radiometer (ASTER) imager are shown in

figures 1(e) and 1(f), respectively. Landslides covered many of the steep hills around

the city. Nearly all the mountains that flanked the city collapsed in several places. Insome cases, wide sections of the mountain, more than one kilometre in width, slid

into the valley below.

3. Methodology

In this section we describe a simple method of change detection. The method is based

on the use of the point similarity measure proposed by Rogelj and Kovacic (2003).

Point similarity is defined as a measure which is used to calculate the similarity of

individual pixels. The basic idea of the proposed method of change detection is that

pixels affected by landslides will be maximally dissimilar, that is, the value of

6034 S. Khairunniza-Bejo et al.


Figure 1. Before and after landslide images. Location of the main damaged area in every caseis indicated by a box. (a) Images of Niigata, Japan dated 13/8/2004. (b) Images of Niigata,Japan dated 24/10/2004. (c) Images of Aceh, Indonesia dated 13/12/2004. (d) Images of Aceh,Indonesia dated 29/12/2004. (e) Images of Muzaffarabad, Pakistan dated 14/11/2000. (f) Imagesof Muzaffarabad, Pakistan dated 27/10/2005.

Landslide detection and identification 6035


similarity of these pixels will be low. We use mutual information as the measure of

similarity. Point similarity based on mutual information was used by Rogelj et al.

(2003) as a way of measuring similarity when registering two medical images.

Mutual information of the overlapping parts of the two images was computed using:

MAB ¼XpA

XpA

PABðpAi ; p

Bi Þ log10

PABðpAi ; p

Bi Þ

PAðpAÞPBðpBÞ

� �; (1)

where PAðpAÞ is the normalized histogram of grey values of the reference image,PBðpBÞ is the normalized histogram of grey values of the sensed image and

PABðpAi ; p

Bi Þis the normalized joint histogram of the grey values pA

i and pBi which

correspond to the same pixel i. Local mutual information is derived from the global

mutual information. Equation (1) may be rewritten as:

MAB ¼XpA

XpB

Ni

Nlog10

PABðpAi ; p

Bi Þ

PAðpA; pBÞ

� �

¼ 1

N

Xc

log10

PABðpAc ; p

Bc Þ

PAðpA; pBÞ

� �; ð2Þ

where Ni is the number of occurrences of intensity pair ðpAi ; p

Bi Þ and N is the number of

pixels in the overlapping part of the two images. Furthermore, PABðpAc ; p

Bc Þ is the

normalized joint histogram of grey values pAc and pB

c which correspond to the same

coordinate c. Note that the final summation is taken over the spatial image coordinates

instead of the intensities. Therefore, the global similarity MAB can be treated as anaverage of point similarities SMAB

, defined for each pixel at coordinate c:

MAB ¼1

N

Xc

SMAB; (3)

where

SMAB; log10

PABðpAc ; p

Bc Þ

PAðpAÞPBðpBÞ

� �: (4)

We begin by calculating the point similarity of a pixel in every coordinate in the

image. Thus, every pixel c is represented by its value of mutual information. We notethat SMAB

> 0 when PABðpAc ; p

Bc Þ > PAðpAÞPBðpBÞ. This implies that the information

of a pixel at coordinate c in image A can be predicted using the information of the pixel

at coordinate c in image B. Thus, since they are related to each other, point c is defined

as a point with high value of similarity. SMAB¼ 0 when PABðpA

c ; pBc Þ ¼ PAðpAÞPBðpBÞ.

It means that the pixel at coordinate c in image A does not give any information about

the pixel at the same coordinate in image B. They are totally independent. Either pixel

may take any value at all (Petrou 2004). We note that SMAB< 0 when

PABðpAc ; p

Bc Þ < PAðpAÞPBðpBÞ. This means that values pA and pB may have very high

frequencies, but they do not occur together. This is due to a high dissimilarity of the

pixel at coordinate c in images A and B. Thus, based on this definition, we threshold

our local mutual information image as follows:

GðcÞ ¼ 255 if SMAB> 0

0 if SMAB�0

� �; (5)



where G(c) is the binary value of the pixel at coordinate c in the thresholded image.

Therefore, the changed areas are represented by the black pixels in a binary image.

Simple thresholding is not sufficient to identify the true changes. There will be some

undesirable change blobs detected. Therefore, in order to eliminate all undesirable

change blobs, we filtered out these pixels using an area-based filter. We assumed thelandslide to be the largest changed region(s) in the image. To apply it, we first grouped

the connected changed pixels into regions using connected component analysis. The

number of pixels that made up every region was then calculated. The landslide area

was identified as the region or regions with the largest area.

4. Experimental results

Figure 2 shows results of the landslides being detected using the local similaritymeasure method. The binary change image of Niigata is shown in figure 2(a). From

this figure, we can see that the changed locations, which are represented by black

pixels, have very high concentration in the region of the landslide covering the road

along the river. The proposed change detection method is used to detect landslides

with a very large extent. Therefore the threshold value is selected by referring to the

number of pixels in the changed regions. The region or regions with higher numbers of

pixels will be identified as the landslide area. Figure 2(b) shows a histogram of the

changed regions. Regions which contained the number of pixels at the right of thehistogram will be considered as the large region. Therefore, in order to identify the

landslide area, we chose a threshold value of 4000 pixels to the changed regions. As a

result, regions which contained less than 4000 pixels were eliminated. Only two

regions were left. In order to extract these regions properly, we dilated the pixels

inside these regions seven times with a 3 � 3 pixels structuring element. The result is

shown in figure 2(c). The area threshold chosen and the number of dilations per-

formed may appear to be arbitrarily chosen here. However, the choice is made based

on the resolution of the image and the size of the changed regions one seeks to identify.Results of the landslide in Acheh are shown in figure 3. The binary change image of

Aceh is shown in figure 3(a). The area histogram of the regions identified using

connected component analysis is shown in figure 3(b). From this histogram, a thresh-

old value of 1000 pixels was chosen to eliminate the changed regions which contained

less than 1000 pixels. As a result, only two regions were left. Pixels in these regions

were then dilated two times with a 3� 3 pixels structuring element to approximate the

landslide region as shown in panel (c).

Results of the landslide in Muzaffarabad are shown in figure 4. The binary changeimage of Muzaffarabad is shown in figure 4(a). This figure shows that there is a lower

concentration of changed pixels. Thus, it is clearly indicated that both images are

quite similar to each other. The area histogram of the regions identified using

connected component analysis is shown in figure 4(b). From this histogram, a thresh-

old value of 90 pixels was chosen to eliminate the changed regions which contained

less than 90 pixels. The region left is shown in figure 4(c). From this figure, we can see

that the change region was detected at the upper right of the image which is outside the

indicated box. The result shows that this method cannot be used to detect the land-slides in Muzaffarabad. This is because this landslide is too small compared with the

size of the imagery used. To improve the effectiveness of the detection, we decided to

crop the large images and keep only sub-images of size 100 � 100 pixels, 200 � 200

pixels and 300 � 300 pixels which we know contain the landslide, and repeat the



process. In all cases, the result showed that the largest identified regions were inside

the boxes indicating the landslide area. These results are shown in figure 5.

Figure 6 shows results of the landslides being detected using the image differencing

method. In the case of landsides in Niigata, it has been shown in figure 6(a) that the

image differencing method detects many changes in the river that are not at the

location along the river where the exact location of the landslides had occurred.

This is because the grey-level values of the pixels before and after the landslide

Figure 2. Landslide in Niigata, Japan. (a) Binary change image produced by thresholding thelocal mutual information of the image. (b) An area histogram of the changed regions. (c) Pixelsin the regions which contain 4000 or more pixels in (a) are dilated seven times with a 3� 3 pixelsstructuring element.



occurrence in this area are not similar. Therefore, although there are no physical

changes, the image differencing method will detect these changes. In the case of

landslides in Pakistan, the image differencing method only can detect the landslides

when the sub-images of size 100 � 100 were used. This is shown in figure 7(a).

From the results, we found that both change detection methods cannot be used to

detect and quantify small landslides when the size of the imagery used is too large

Figure 3. Landslide in Aceh, Indonesia. (a) Binary change image produced by thresholdingthe local mutual information of the image. (b) An area histogram of the changed regions. (c)Pixels in the regions which contain 1000 or more pixels in (a) are dilated two times with a 3� 3pixels structuring element.



compared with the landslide areas. Table 1 summarizes the ratio of the approximate

size of the landslide over the size of the imagery used. Since the sizes of landslides fromthe ground truth data were not available, we assumed the size of each landslide to

be the same as the size of the box indicating it. From this table, it can be concluded

that the local similarity measure method works well when the ratio between the

landslide and the imagery used is approximately 1 to a few tens, and that the method

breaks down when this ratio is of the order of 1 to 100. This is because the landslide

has to be small enough for the global mutual information not to be affected by its

0 20 40 60 80 100 1200

2

4

6

8

10

number of pixels

num

ber

of r

egio

ns

(a)

(b)

(c)

km

km

Figure 4. Landslide in Muzaffarabad, Pakistan. (a) Binary change image produced by thresh-olding the local mutual information of the image. (b) The area histogram of the changedregions. (c) Regions which contain 90 or more pixels in (a).



presence (and so the landslide to appear as a significant anomaly), and large enough

for the anomaly created by the landslide to be larger than the normal variation

observed either due to genuine small changes in the image, or due to noise in the

calculation of the statistical quantities. However, the image differencing method does

not give a consistent ratio of the detected landslide area with size of imagery used. Thisis because the image differencing method only depends on the grey-level value of the

image, not global or local information. Results of the detected change areas show that

some mis-detection occurred in both methods. Therefore, the false alarm rate was

Figure 5. Results of the local similarity measure method for the subimages of the landslides inMuzaffarabad, Pakistan. The largest region in the images has been dilated with a 3 � 3 pixelsstructuring element. (a) 100 � 100 pixels size of subimage. One time erosion and one timedilation. (b) 200 � 200 pixels size of subimage. Two time dilation and eight time erosion.(c) 300 � 300 pixels size of subimage. Two time dilation and eight time erosion.



Figure 6. Results of the image differencing method. (a) Binary change image of Niigata, Japanproduced by thresholding the difference image with the threshold value of 100. (b) Pixels in theregions which contain 35 000 or more pixels in (a). (c) Binary change image of Aceh, Indonesiaproduced by thresholding the difference image with the threshold value of 50. (d) Pixels in theregions which contain 1600 or more pixels in (c) are dilated two times with a 3 � 3 pixelsstructuring element. Results of the image differencing method. (e) Binary change image ofMuzaffarabad, Pakistan produced by thresholding the difference image with the thresholdvalue of 15. (f) Pixels in the regions which contain 1600 or more pixels in (e).



calculated by dividing the number of false alarms by the number of alarms. It has been

shown that the proposed method gives a smaller false alarm rate of only 16%,

compared to that of the image differencing method, which is 50%. In terms of CPU

time, the local similarity measure method has higher computational cost compared to

the image differencing method. The method took 349 s for the Niigata image, 7 s forthe Aceh image and 2 s for the Muzaffarabad image to complete the whole process of

Figure 7. Results of the image differencing method for the subimages of the landslides inMuzaffarabad, Pakistan. The largest region in the images. (a) 100� 100 pixels size of subimage.The largest region in the image has been dilated one time with a 3� 3 pixels structuring element.(b) 200 � 200 pixels size of subimage. (c) 300 � 300 pixels size of subimage.



detection and identification. Meanwhile, the image differencing method took only 1 s

for the Niigata image and , 1 s for the Aceh and Muzaffarabad images.

5. Conclusions

In this article we presented a simple method of change detection and identificationusing a local similarity measure based on mutual information and image thresholding.

It has been shown that the local similarity measure method gives more reliable results

of detection and identification compared to the image differencing method. Since

mutual information is used as a measure of similarity, this method can be used to

detect landslides even from different modality images. We demonstrated that this

method can be used to detect changes in natural and remote areas. The area-based

filter can be used to identify the most significant changed areas. Therefore, this

method is suitable for detecting extensive landslides, where the area covered by thelandslide region is also identified as the largest area among the other changed regions.

The use of mathematical morphology gave a good approximation of the area where

the landslide had occurred. The algorithm for detecting the changed pixels can be fully

automatic. Post-processing for identifying coherent changed regions, however,

requires the choice of thresholds and this is semi-automatic. This part may also be

automated if prior knowledge about the scale of the image and the size of the expected

landslide is considered. The method is superior to the simplest method of change

detection. However, the method does not include a mechanism that allows thedistinction between a landslide and any other change on the surface of the Earth

that causes changes in the appearance of the image. Such a mechanism should

probably involve a higher level of knowledge which includes the slope and shape

information and it should probably operate in conjunction with an expert system.

Since the capability of the proposed method depends on the ratio of the unchanged to

changed pixels in the image, this semi-automatic method has the potential to detect

small size landslides in high spatial resolution satellite images if the ratio between the

unchanged to changed pixels in the image is in the order of one to a few tens. Withfurther in-depth study of this method, it could have the potential to be used to classify

landslide zones, such as zone of initiation, zone of transportation and zone of

accumulation.

ReferencesCHENG, K.S., WEI, C. and CHANG, S.C., 2004, Locating landslides using multi-temporal satellite

images. Advances in Space Research, 33, pp. 296–301.

Table 1. Comparison on the ratio of the approximation of the landslide area with size ofimagery used.

CaseImage size

(pixel)Box size(pixel) Ratio

Local similaritymeasure

Imagedifferencing

Niigata 1033 �1044 450 � 720 1:33.33p �

Aceh 270 � 270 170 � 130 1:32.99 p pMuzaffarabad 565 � 768 40 � 65 1:166.89 � �Sub-image 1 100 � 100 40 � 65 1:3.85

p pSub-image 2 200 � 200 40 � 65 1:15.38 p �Sub-image 3 300 � 300 40 � 65 1:34.62 p �



HERVAS, J., BARREDO, J.I., ROSIN, P.L., PASUTO, A., MANTOVANI, F. and SILVANO, S., 2003,

Monitoring landslides from optical remotely sensed imagery: the case history of Tessina

landslide, Italy. Geomorphology, 1346, pp. 1–13.

LIN, W.T., CHOU, W.C., LIN, C.Y., HUANG, P.H. and TSAI, J.S., 2005, Vegetation recovery

monitoring and assessment at landslides caused by earthquake in Central Taiwan.

Forest Ecology and Management, 210, pp. 55–66.

NICHOL, J. and WONG, M.S., 2005a, Satellite remote sensing for detailed landslide inventories

using change detection and image fusion. International Journal of Remote Sensing, 9,

pp. 1913–1926.

NICHOL, J. and WONG M.S., 2005b, Detection and interpretation of landslides using satellite

images. Land Degradation and Development, 16, pp. 243–255.

OSTIR, K., VELJANOVSKI, T., PODOBNIKAR, T. and STANCIC, Z., 2003, Application of satellite

remote sensing in natural hazard management: the Mount Mangart landslide case

study. International Journal of Remote Sensing, 24, pp. 3983–4002.

PETROU, M., 2004, Image Registration: an overview. Advances in Imaging and Electron Physics,

130, pp. 243–291.

ROGELJ, P. and KOVACIC, S., 2003, Point similarity measure based on mutual information. In 2nd

International Workshop on Biomedical Image Registration, 23–24 June, 2003, Philadelphia,

USA, pp. 112–121 (New York: Springer-Verlag).

ROGELJ, P., KOVACIC, S. and GEE, J.C., 2003, Point similarity measures for non-rigid registra-

tion of multi-modal data. Computer Vision and Image Understanding, 92, pp. 112–140.

ROSIN, P.L. and HERVAS, J., 2005, Remote sensing image thresholding methods for determining

landslide activity. International Journal of Remote Sensing, 26, pp. 1075–1092.

WHITWORTH, M.C.Z., GILES, D.P. and MURPHY, W., 2005, Airbone remote sensing for landslide

hazard assessment: a case study on the Jurassic escarpment slopes of Worcestershire,

UK. The Quarterly Journal of Engineering Geology and Hydrogeology, 38, pp. 197–213.

YAMAGUCHI, Y., TANAKA, S., ODAJIMA, T., KAMAI, T. and TSUCHIDA, S., 2003, Detection of a

landslide movement as geometric misregistration in image matching of SPOT HRV

data of two different dates. International Journal of Remote Sensing, 24, pp. 3523–3534.



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International Journal of Remote Sensing Local similarity ... IJRS 2010.pdf · To cite this Article Khairunniza-Bejo, Siti , Petrou, Maria and Ganas, Athanassios(2010) 'Local similarity