DAMAGE DETECTION DUE TO THE TYPHOON HAIYAN FROM HIGH-RESOLUTION SAR IMAGES

Wen Liu a, Masashi Matsuoka b, Bruno Adriano c, Erick Mas d and Shunichi Koshimura d

a) Department of Urban Environment System, Chiba University, Japan b) Department of Built Environment, Tokyo Institute of Technology, Japan

c) Graduate School of Engineering, Tohoku University, Japan d) International Research Institute of Disaster Science, Tohoku University, Japan

ABSTRACT

A strong typhoon “Haiyan” affected Southeast Asia on November 8, 2013, caused gigantic destruction in the Philippines. In this study, two pre- and one post-event COSMO-SkyMed SCSB data were used to detect the damaged area around Tacloban City, Leyte Island. First, the severe damaged areas were detected according to the difference between the pre- and post-event speckle divergence values. Then the pre- and co-event coherence (NDCI) and correlation coefficient (NDCOI) were calculated from the three temporal data. The relationships between the four building damage levels and NDCI or NDCOI value were obtained by introducing the visual interoperation result. Using this relationship, the possibility of each damage class was estimated in the whole urban area.

Index Terms—COSMO-SkyMed data, coherence, speckle divergence, correlation coefficient, building damage

1. INTRODUCTION

Typhoon “Haiyan” which is also called as “Yolanda” affected the Philippines on November 8, 2013. Due to the strong storm with the maximum speed higher than 250 km/h and heavy rainfall, it caused catastrophic damage throughout most of Leyte and Samar Islands. According to the report of National Disaster Risk Reduction and Management Council (NDRRMC) [1], more than 6 thousands people were killed and 1 million houses damaged. Limited by the local environment to respond the widespread damaged area, it was difficult to grasp the damage condition by the field survey [2]. Although the optical satellite images could provide more detailed surface information, most of them were covered by cloud due to the weather condition. Thus, the pre- and post-event COSMO-SkyMed (CSK) data are used to detect the damaged area in this study. First, an urban index was proposed by composing the backscattering intensity and the local speckle divergence (SPD) [3]. The severe damaged area was extracted by the changes of the urban index before and after the typhoon. Then two damage ratio models were built

using the normalized change of coherence and correlation coefficients. By applying the damage evaluation models, the possibilities of four damage levels were estimated in the urban area. Finally, the accuracy of the detected result was discussed by comparing with the visual interpretation.

2. IMAGE DATA AND PRE-PROCESSING Two pre- and one post-event CSK data cover the north coastal zone of Leyte Island, as shown in Fig. 1 where the area within the red polygon was used for the analysis. The pre-event data were taken on August 19 and September 20, 2013, while the post-event data was taken on November 20, 2013 (one week after the typhoon passed), respectively. The incident angle at the center of the images was 46.1°. All images were captured with HH polarization and in the descending path. The images were acquired in the STRIPMAP HIMAGE mode, and provided as Single-look-complex and Slant-range (SCSB) data in processing level 1A. The spatial spacing is 1.6 m in the range and 1.9 m in the azimuth directions.

Several preprocessing approaches were applied to the initial data. First, the three temporal data were registered in a sub-pixel level. Considering to the small house scale in Tacloban, the number of looks was set as 1 in both the range and azimuth directions to keep the high resolution. Three indices such as urban index (UI), coherence index (NDCI)

Fig. 1 Path of Haiyan’s storm center (left); the coverage of the CSK data (red) and the target area around Taclonban City (blue) in this study (right).

and correlation coefficient index (NDCOI) were mainly used to extract and classify the damage. To calculate the coherence, the SCSB data were used directly. The UI and correlation coefficient were obtained from the geo-coded intensity images. The 90 m SRTM dataset (Ver. 2) was applied for the geo-coding and radiometric calibration approaches. Finally, three CSK sigma naught images were obtained and shown in Fig. 2(a) as a color composite. All of those preprocessing approaches were carried out by the software ENVI/SARscape. After the image pre-processing, the geo-coded images were re-sampled as 1.25 m/pixel.

3. EXTRACTION OF SEVERE DAMAGED AREA

Speckle divergence (SPD) is a texture filter calculated by applying the local standard deviation versus the local average intensity, which can be calculated by Eq. (1) [3].

cSPD 1 (1)

where σ is the standard deviation and μ is the average value within the local window; c is the total number of looks in the azimuth and range directions, which is 2 in this study.

Due to the high corner reflection of buildings, the built-up area shows the higher SPD value than other land covers. However, the contour of water also shows high SPD value for the radical changes at the borderline between land and water surfaces. German Aerospace Center (DLR) developed an Urban Footprint Processor (UFP) to extract the urban area automatically [4]. The urban area was classified using the amplitude and SPD value in the slant range by applying the training samples. In this study, an urban index (UI) was proposed by composing the sigma naught backscatter and SPD values, which could be obtained by Eq. (2).

SPD

SPD

I

I SPDIUI (2)

where I is the sigma naught value; σ is the standard deviation and μ is the average value of sigma naught and SPD in the whole target area.

First, the SPD values were calculated from the three temporal CSK images in the Digital Number (DN) level. Considering to the average building size, three different window range of 15 ×15, 21 × 21, and 41 × 41 pixels were applied. When the window size was set as 15 ×15 pixels, only the outlines of buildings were obtained; when the window size was set as 41 × 41 pixels, several small housings showed low SPD value. Thus, 21 × 21 pixels (about 25 m) window was adopted to calculate the SPD value. The average value for the pre-event SPDs were similar as 0.42 and the standard deviation was 0.54. The average value for the post-event SPD was 0.40 and the standard deviation was 0.51.

Then three temporal UI values were calculated by Eq. (2). The average value of backscattering coefficient from the image taken on August 19 was -12.36 dB, while it was -12.14 dB on September 20 and -10.29 dB on November 20. The three temporal standard deviations were similar as about 6.6 dB. After normalizing the backscattering coefficient and SPD values by their average and standard deviation respectively, three temporal UI values were obtained. The UI values on August 19 and September 20 were averaged as one pre-event one, whose range is from -3.42 to 18.00. The range of post-event UI values is from -4.04 to 21.07. A color composite of the pre- and post-event UI values is shown in Fig. 2(b). Comparing with Fig. 2(a), the built-up area could be identified more easily. By applying both the backscattering coefficient and SPD values, the outlines of water surface were weaken and could be distinguished from built-up area.

Since the pre-event UI image is allocated for the green and blue bands while the post-event one for the red band, the areas with cyan color are considered as the washed away areas with the decreasing of UI value. The red areas with the increasing of UI values are considered as debris. A difference image was

(a) (b) (c) (d)

Fig. 2 Color composite of three temporal sigma naught intensity images (a); a color composite of the pre- and post-event urban indices (UIs) (b); the difference between the pre- and post-event UIs (c); the built-up area extracted according to the pre-event UI (c);.

obtained by subtracting the post-event UI value from the pre-event shown in Fig. 2(c). It was confirmed that the buildings near the coast were destroyed due to the big waves during the typhoon passing, which were detected as positive changes (red). Most of the urban areas were covered by debris and detected as negative changes (blue).

In addition, the built-up area was extracted according to the pre-event UI value. By comparing with the optical images on Google Earth, the areas with higher UI value than 1.5 were considered as built-up area. According to the window size used for the SPD calculation, the extracted areas smaller than 440 pixels were removed as commission error. Then the holes within the extracted area were filled. Finally, a built-up area mask was built and shown in Fig. 2(d). Overlaying the built-up mask on the Open Street Map, most of the urban area could be confirmed. Several small housings enclosed by trees or with the high density could not be extracted by the proposed urban index.

4. CLASSIFICATION OF BUILDING DAMAGE LEVEL

A classification for building damage level was carried out by the coherence and correlation coefficient changes before and after the typhoon.

The pre- and the co-event coherence values were calculated in the slant range images from the complex data. The pre-event coherence (γa) was obtained from the images taken on August 19 and September 20. Since the baseline distance between the observations on August 19 and November 20 was similar to the pair of pre-event observations, the co-event coherence (γb) was obtained by those two images. After the typhoon passed, the ground surface changed much due to the building damages and debris. The co-event coherence decreased drastically compared with the pre-event coherence. To estimate the accurate decreasing in coherence, a normalized coherence difference (NDCI) was calculated by Eq. (3) and shown in Fig. 3(a).

ba

baNDCI (3)

The changes of correlation coefficient were also obtained from the same pairs in the ground range. The pre-event correlation coefficient (Ca) was calculated from the sigma naught backscatter images on August 19 and September 20 by Eq. (4). The co-event coefficient (Cb) was calculated from the images on August 19 and November 20. A decreasing on the correlation coefficient was also confirmed after the typhoon passed. The normalized correlation coefficient difference (NDCOI) was obtained by Eq. (5) and shown in Fig. 3(b). Since the Ca and Cb were between -1.0 and 1.0, they were added 1 to move into 0 and 2 respectively.

N

i

N

i

N

i

N

i

N

i

N

i

N

i

IbIbNIaIaN

IbIaIbIaNC

2222 )()(

(4)

2)1()1()1()1(

ba

ba

ba

ba

CCCC

CCCC

NDCOI (5)

The coherence and the correlation coefficient were both calculated by the 7 × 7 pixels window. The NDCI was between -1.0 and 1.0, while the NDCOI was between -0.7 and 0.8. The positive value means that the surface had changed. Then a visual interpretation result of building damage classification was used as the training samples. The classification was carried out by the Copernicus Emergency Management Service (GIO EMS) based on the high-resolution optical images and the building footprints [5]. 6,211 buildings are classified into five classes such as “All affected”, “Highly affected”, “Moderately affected”, “Possibly affected”, and “No affected”, and their footprints are also shown in Fig. 3 by black polygons. Since only 23 buildings were classified as “No affected”, we merged “Possibly affected” and “No affected” classes into one class. Although the number of “All affected” buildings was much larger than other classes, these buildings were obviously smaller. Therefore, the number of samples from each damage class was aligned in a pixel level. 30,000 pixels were randomly selected for each damage class from the slant range NDCI image, while 80,000 pixels from the ground range NDCOI image. The selected pixels were sorted in the increasing order according to the NDCI and NDCOI values. Then the average value of NDCI was calculated from each 3,000-pixel dataset respectively. At the same time, the percentage of the number of pixels in each the damage class within 3,000-pixel dataset was counted. For NDCOI, the average value and the ratio of four damage classes were also calculated from each 8,000-pixel dataset. According to the relationships between the ratio of damage level and the NDCI or NDCOI values, two damage models were built and the results are shown in Fig. 4. For the NDCI value, the ratio of

(a) NDCI (b) NDCOI

Fig. 3 Footprints of 6,211 sample buildings overlaying on the NDCI values in the slant range image (a) and on the NDCOI value in the ground range image (b).

damage level was built as a two-dimension polynomial regression. For the NDCOI value, the ratio was fitted as a linear regression. It could be confirmed that the damage level becomes higher when the average value goes larger in both the NDCI and NDCOI models.

The obtained models were applied to the whole built-up area to estimate the building damage levels. The urban mask extracted by the procedure described in the previous section was used to remove the rural area. Then the possibilities for four building damage levels were calculated from NDCI and NDCOI values respectively.

A comparison with the visual results, the UI difference obtained in the previous section and the possibilities of more than highly affected buildings from NDCI and NDCOI values are shown in Fig. 5. The location of the close-up area is shown in Fig. 2(d) by the red rectangle. The areas with UI difference higher than 3.0 shown in Fig. 5(b) by the red color, are considered as the more than highly affected area. Two most destroyed areas near the coast were extracted successfully from all the three indices. The southern highly affected area is also good agreement with the result of visual interpretation. The differences of possibilities between more than highly affected area and less than moderately affected area were larger from NDCOI than that from NDCI. Since the coherence is sensitive on surface changes, it changes much even for the small damages. Thus, NDCOI looks more suitable to classify the building damage levels in this case.

5. DISCUSSION AND CONCLUSIONS Urban index (UI), coherence (NDCI) and correlation coefficient (NDCOI), three indices were proposed in this study to extract the damaged area and estimate the possibility for the damage level. More than highly affected area could be extracted successfully from all the indices. The pre-event built-up area were extracted by the threshold of UI. By applying several results of visual interpretation as the training data, the models to estimate the probabilities of four building damage levels were developed according to the NDCI and NDCOI. Comparing with the visual interpretation, the NDCOI shows higher quality to classify the damage levels than the NDCI.

6. ACKNOWLEDGEMENT COSMO-SkyMed data was owned by the Italian Space Agency

(ASI) and provided by Japan Space Imaging Corporation. Building footprints were downloaded from the website of GIO EMS. This research was supported by the J-RAPID program from Japan Science and Technology Agency (JST).

7. REFERENCES

[1] E. Mas, J. Bricker, S. Kure, B. Adriano, C. Yi, A. Suppasri, and S. Koshimura, “Field survey report and satellite image interpretation of the 2013 Super Typhoon Haiyan in the Philippines,” Natural Hazards and Earth System Sciences Discussion, Vol. 2, pp. 3741-3767, 2014.

[2] NDRRMC, “SitRep No. 92 Effects of Typhoon “YOLANDA” (HAIYAN)”, 2014. Available: http://www.ndrrmc.gov.ph/ attachments/article/1125/NDRRMC%20Update%20re%20Sit%20Rep%2092%20Effects%20of%20%20TY%20%20YOLANDA.pdf

[3] J.-S. Lee, K. Hoppel, and S.A. Mango, “Unsupervised estimation of speckle noise in radar images,” International Journal of Imaging Systems and Technology, Vol 4, No. 4, pp. 298-305, 1992.

[4] T. Esch, Michael Thiel, Andreas Schenk, A. Roth, A. Müller, and S. Dech, “Delineation of urban footprints from TerraSAR-X data by analyzing speckle characteristics and intensity information”, IEEE Transactions on Geoscience and Remote sensing, Vol. 48, No. 2, 2010.

[5] GIO EMS: http://emergency.copernicus.eu/mapping/list-of-components/EMSR058

Fig. 4 Models of the possibility of four building damage levels, according to the relationship between the damage and the NDCI or NDCOI values obtained for training samples.

(a) (b)

(c) (d)

Fig. 5 Classification of five building damage levels by visual interpretation from GIO EMS overlapping on the intensity image taken on Aug. 19, 2013 (a); differences of urban index before and after the typhoon (b); estimated possibility for more than highly affected area from NDCI (c) and NDCOI (d) values.