The use of satellite remote sensing within the domain of natural hazards and disasters has recently received widespread attention [1
]. This novel sensing technology allows us to achieve a rapid damage assessment due to its fast response and wide field of view. Particularly, synthetic aperture radar (SAR), which can provide a high-resolution image irrespective of weather conditions, has the potential to detect damaged buildings with high accuracy [3
]. The periodicity of satellites in orbit enables computing changes between a pair of SAR images [4
]. Moreover, the recent emergence of freely available SAR image, such as Sentinel-1 data, has opened up the possibility for more users to conduct urgent observations the Earth surface [6
]. Several authors have addressed the problem of retrieving building damage information from SAR images to aid disaster relief activities.
The detection of damaged buildings from SAR images is divided into two processes: computing change features and constructing a classification model. Previous studies have suggested that a direct pixel comparison leads to poor change detection accuracy because it mainly focuses on the spectral values and mostly ignores the spatial context [8
]. Calculating change features over a window is effective in considering the spatial distribution of image pixels [9
]. Many authors have successfully applied change features derived from subimages to detect damage caused by natural disasters [10
]. Moreover, using building footprint data and high-resolution SAR images enable detecting building damage at the building unit scale [15
]. After computing change features, a classification model needs to be constructed. Many studies have proposed thresholds and parameters for damage detection models based on experience, with limited success [16
]. A supervised machine learning approach enables overcoming such limitations. This approach allows us to construct a high-dimensional discriminant function [17
]. The quality and quantity of training data play a pivotal role in supervised machine learning. These training data can be obtained through visual inspection of high-resolution optical images [19
]. Moreover, by replacing training samples with probabilistic information derived from the spatial distribution of the hazard, databases of previous disasters can be used for constructing a discriminant function [20
From the perspective of assessing monetary damages, damaged buildings should be sorted into detailed classes. For seismic events, multiclass classification of damaged buildings has been widely studied in the field of satellite remote sensing [21
]. According to previous studies, the relationship between the damage grade and the building appearance in remote sensing imagery differs depending on the type of data used [22
]. However, identifying lower damage grades at the building unit scale has been difficult, regardless of the sensor used [23
]. Thus, these grades tend to be aggregated as one class to define a detectable classification scheme in the case of earthquakes. There are few reports about the multiclass classification of damaged buildings for tsunami-induced damage. Buildings in tsunami affected areas generally suffer damage on the lower part of the building sidewall, except for buildings that were washed away. Using optical sensor images, which allow us to inspect only roof conditions, it is difficult to conduct a detailed classification immediately after the disaster. Meanwhile, SAR is seemingly effective in detecting such changes due to its side-looking geometry [26
]. However, an automatic classification of buildings with tsunami-induced damage in SAR images remains a major challenge. Although the multiclass classification model that considers the statistical relationship between the change ratios in areas with high backscattering and in areas with building damage has been proposed, lower dimensional feature space of the classifier has limited the generalization ability [27
]. Unfortunately, previous studies have not provided consistent guidance on how to define a detectable classification scheme for SAR images. It is clear that further research is necessary to better understand multiclass classification for buildings with tsunami-induced damage.
The objective of this paper is to propose a new model for classifying building damage into detailed classes using pre- and post-event high-resolution SAR images. By evaluating the generalization ability of the model with various classification schemes, we demonstrate the limitations and potentials of multiclass classification of buildings with tsunami-induced damage. To perform satisfactory multiclass classification, the model considers change features derived from several sizes of pixel windows. In previous studies, a specific size of pixel window is selected due to the difficulty in considering several window sizes simultaneously. The selected window size differs depending on the region of the world and the quality of images [28
]. For example, when analyzing building damage caused by the 2011 Great East Japan Earthquake and Tsunami from TerraSAR-X images, a
window is typically used [15
]. However, using a single window size raises a question about the accuracy of derived change features. We would like to stress the need for constructing a model that can consider a high-dimensional discriminant function derived from several sizes of pixel windows. To this end, our classification model uses a support vector machine (SVM) for optimally weighting each change feature. Due to its outstanding generalization ability, SVM has been used in the field of remote sensing [30
]. To avoid the overfitting problem, which has been an obstacle to using a high-dimensional feature space, generalization parameters are determined based on the combination of a coarse grid search and a fine grid search. We tested the performance of building damage classification using pre- and post-event TerraSAR-X images taken in the 2011 Great East Japan Earthquake and Tsunami.
2. Study Area and Dataset
This study focused on the coastal area of Sendai city (Figure 1
) in Miyagi prefecture, Japan. This city was devastated by the Great East Japan Earthquake and Tsunami on 11 March 2011. TerraSAR-X data acquired on 20 October 2010 and 12 March 2011 were used to detect damaged buildings. The images were captured in Stripmap mode and delivered as Enhanced Ellipsoid Corrected (EEC) products. The details of the acquisition of SAR images are shown in Table 1
The ground-truth data (GTD) of building damage were provided by the Ministry of Land Infrastructure, Transport and Tourism (MLIT) [31
]. These data were released on 23 August 2011, five months after the disaster occurred. The data were referenced at the building footprint and include 11,562 buildings described by seven damage grades (Table 2
): “G7: no damage,” “G6: minor damage,” “G5: moderate damage,” “G4: major damage,” “G3: complete damage,” “G2: collapsed,” and “G1: washed away.”
4. Classification Conditions
The following three analyses were conducted to show the effectiveness of our model and to understand the potentials and limitations of multiclass classification of buildings with tsunami-induced damage. In the analyses, the performance of the proposed classifier was evaluated with various conditions. Conditions of the analyses are summarized in Table 3
. The classification performance was measured by comparing the estimated class of the testing samples with GTD. Precision, recall, and F-score were presented as standard accuracy measures in respective classes [36
]. Precision is the ratio of the total buildings that are correctly classified as a specific class of interest to the total buildings that are classified as the class. Recall is the ratio of the total buildings that are correctly classified as a specific class of interest to the total buildings that are labeled as the class in GTD. F-score is the weighted average of Precision and Recall. Macroaveraging, which treats all classes equally, was used for calculating the average score of each accuracy measure [37
The first analysis evaluated the performance of the proposed classification model with the original classification scheme, which divided damaged buildings into seven grades based on the field survey of MLIT. In total, 300 training samples per class were randomly selected from the study area. Additionally, 300 testing samples per class were randomly selected from the study area.
In the second analysis, reclassifications of original scheme were conducted to evaluated the effect of changing classification scheme. Damaged buildings were reclassified into three classes, as shown in Table 4
. Note that the reason these five schemes were considered is described in Section 5.2
. Generalization ability of the classifier was evaluated with the each scheme. The numbers of training and testing samples were set to 300 and 500, respectively.
Finally, the effectiveness of the proposed method, which uses twelve change features derived from
pixel windows, was verified through comparison with a model that uses only two change features derived from a
pixel window. Both models constructed a discriminant function based on the process in Section 3.4
, and referred to the reclassification scheme that was the most detectable scheme in the second analysis. The number of training samples that were randomly selected in the study area was changed in the range from 50 to 400 per class. The classification ability of the function was tested with 400 independent testing samples per class. To evaluate the robustness of the classifier, this sequence of steps was repeated 50 times. The average and standard deviation of the classification ability were computed.
In this paper, we have developed multiclass building damage classification model for tsunami impact. This model considers a high-dimensional feature space derived from several sizes of pixel windows. Taking advantage of SVM, we can construct a classification model from much smaller training samples.
To reveal the possibilities and limitations of multiclass classification of buildings with tsunami-induced damage, the proposed model was tested on the 2011 Great East Japan Earthquake and Tsunami. In the first analysis, classification performance was evaluated with the original seven-class classification scheme. Our analysis found that the proposed model can easily detect the most severely damaged buildings and undamaged buildings in tsunami affected areas. On the other hand, in the case of earthquake-induced damage, undamaged buildings can rarely be distinguished from slightly damaged buildings. This finding will advance understanding of the difference between tsunami-induced damage and earthquake-induced damage. The analysis also implies that detectability of other classes are limited due to the vague definition of the classes. Thus, we should apply a detectable classification scheme to ensure stable classification performance. To this end, the performance of the classifier was tested with various reclassification schemes in which damaged buildings were divided into three classes. Our study showed that aggregating damage grades (G2–G6) that can rarely be identified from SAR images increases the detectability of the defined three-class classification scheme. Notably, this classification scheme allows the proposed model to provide high generalization ability even with 50 training samples per class. The proposed method enables reducing the time required to retrieve training data in urgent situations.
Our findings open new possibilities not only for tsunami-induced damage detection but also for other change detection tasks. The proposed model, which extracts change features over several sizes of pixel windows and constructs a discriminant function using SVM, can be applied to other tasks, such as urban monitoring and earthquake damage detection. This possibility needs to be investigated in future studies.