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MYU Tokyo
S & M 3449

A Novel Texture Feature Based on Fourier Transformfor Building Damage Recognition from PolSAR Data
Wei Zhai,1,2,3 Yaxin Bi,3* Xiaoqing Wang,2 and Xiang Wang4
1Lanzhou Institute of Geotechnique and Earthquake, China Earthquake Administration,

No. 450 Donggang West Road, Chengguan District, Lanzhou 730000, China
2Institute of Earthquake Forecasting, China Earthquake Administration,
No. 63, Fuxing Road, Haidian District, Beijing 100036, China
3Faculty of Computing, Engineering and the Built Environment, Ulster University,
York St, Belfast, Northern Ireland, BT15 1ED, United Kingdom
4Hebei Seismological Station, Hebei Earthquake Agency,
No. 262 Huaizhong Road, Yuhua District, Shijiazhuang 050021, China(Received February 14, 2023; accepted September 5, 2023)

Keywords: SAR, Fourier transform, texture feature, building damage, earthquake

Building collapse arising from destructive earthquakes is often the primary cause of
casualties and economic loss. Building damage assessment is one of the top priorities inearthquake emergency work. Quad-polarimetric synthetic aperture radar (PolSAR) data not
only have the advantages of radar imaging being neither exposed to sunlight nor blocked byclouds, but also contain the most abundant information of the four polarimetric channels. Only
using conventional polarimetric decomposition methods may lead to overestimations of thenumber of collapsed buildings and the exaggeration of the degree of earthquake damage. We
proposed a parameter called the sector texture feature of the Fourier amplitude spectrum(STFFAS) to describe frequency-domain texture features based on the Fourier amplitude
spectrum in order to solve the overestimation of earthquake building damage. In addition, we
proposed a scheme to recognize building earthquake damage using only a single post-earthquake
PolSAR image combined with STFFAS and the improved Yamaguchi four-component
decomposition method. The 4.14 Ms7.1 Yushu earthquake that occurred in Yushu County,
China, in 2010 is taken as the experimental case. Compared with conventional polarimetricdecomposition methods, this method successfully separated 70.18% of standing buildings fromthe ground objects mixed with collapsed buildings, thus significantly improving the extraction
accuracy and reliability of building earthquake damage information.

1.

Introduction

In the recent decade, earthquakes have occurred frequently around the world, causing huge
losses to human life and property.(1) In urban areas, earthquake casualties are mainly caused bycollapsed buildings. During search and rescue operations for people buried in the rubble, thesooner the information about the collapse of buildings is obtained, the higher the probability of*Corresponding author: e-mail: y.bi@ulster.ac.uk
https://doi.org/10.18494/SAM4346

ISSN 0914-4935 © MYU K.K.
https://myukk.org/

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human survival. Therefore, fast and accurate statistics on the distribution of collapsed buildingsin the earthquake-stricken area is a top priority after an earthquake as well as a guideline fordeveloping other earthquake emergency plans. The main method of traditional earthquake
damage information acquisition is the door-to-door field survey. Although this method has ahigh reliability of information acquisition, it often has a large workload and low efficiency when
carried out in a large area. Remote sensing technology enables the quick monitoring of disasters
on a large scale and has become an important means of earthquake emergency response andpost-earthquake damage assessment. Earthquakes often occur at night or are accompanied bysevere weather conditions, such as overcast, rain and snow, making optical remote sensing
ineffective. However, radar waves are not affected by sunlight and can pass through thick clouds
to obtain information about the disaster areas isolated from the outside world. Synthetic apertureradar (SAR) remote sensing technology can achieve effective ground target imaging under any
weather or climate conditions. Therefore, the use of radar remote sensing for post-earthquake
disaster monitoring seems more secure. In the past, multitemporal SAR data were collected
before and after an earthquake, and the differences between the pre- and post-event data wereused to identify the disaster site. However, the collection of pre-earthquake SAR data is timeconsuming and labor-intensive, and pre-earthquake data of some remote areas are not archived.
In order to save time and labor, we use only post-earthquake single-temporal SAR data torecognize building earthquake damage in the disaster area. In this way, multitemporal dataregistration can also be avoided. Quad-polarimetric SAR (PolSAR) data contain considerably
more information than do single-polarimetric and dual-polarimetric SAR data since PolSAR has
HH, HV, VH and VV polarimetric channels, where H indicates horizontal polarization and V
indicates vertical polarization. Therefore, when only post-earthquake single-scene SAR images
are used for earthquake building damage assessment, we adopt PolSAR images as data sourcesto achieve a higher earthquake damage recognition accuracy and highly reliable earthquake
damage assessment results.
As PolSAR data sources are becoming increasingly available, many scholars and institutions
have applied PolSAR to post-earthquake damage recognition because of the excellent
performance of PolSAR in recognizing ground objects.(2) Since PolSAR data sources arechallenging to obtain in some remote areas and there are no pre-earthquake archived data,
together with the fact that the information contained in PolSAR data is sufficient for singletemporal SAR image earthquake damage recognition, a continuously increasing number ofscholars have begun to use only post-earthquake single-scene PolSAR data to recognize building
earthquake damage in recent years.(3–4) Experiments have proved that recognition results
obtained from a single post-earthquake PolSAR image can meet the requirements of accurate
and speedy earthquake damage recognition,(5) The recognition accuracy is at least as good asthat when using multitemporal SAR data. Wang et al. analyzed the backscattering mechanism of
the damaged buildings in airborne multipolarization SAR images.(6) Li et al. proposed a new
H−α−ρ method based on the circular polarization correlation coefficient for extracting the
spatial distribution of collapsed buildings from RADARSAT-2 fine-mode PolSAR data.(7) Chenet al. presented a modified Freeman decomposition including selective deorientation and surface
scattering characteristic parameter constraint, and used the contribution of the double-bounceSensors and Materials, Vol. 35, No. 10 (2023)

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component to extract the collapsed building spatial distribution from post-disaster
RADARSAT-2 SAR data.(8) Zhai et al. created a novel polarimetric feature parameter, thenormalized difference of the dihedral component (NDDC), to evaluate the degree of building
damage.(9) Ji et al. proposed a new unsupervised damage assessment method in accordance withfour conditions for damaged-building identification.(10) Zhai et al. developed two newpolarimetric features λ_H and H_λ on the basis of entropy and eigenvalues of a T3 matrix todiscriminate five different types of buildings in disaster areas.(11) Liu et al. established a model
based on components obtained by Touzi decomposition for damage assessment mapping in builtup areas.(12) Zhai et al. developed a new polarimetric feature, the variable coefficient of angle
domains based on the Fourier amplitude spectrum parameter (CV_AFI) to assess the degree ofbuilding earthquake damage.(13) Miao et al. improved the Freeman polarization decomposition
method applicable to earthquake-induced building damage identification.(14) Nie et al. created a
new polarization feature—the maximal power contrast (MaxC) feature—via the optimization of
the polarimetric contrast enhancement (OPCE) matching algorithm for identifying collapsedbuildings.(15) Texture features are as important as polarimetric features contained in PolSAR
data, and in many cases, exhibit even stronger recognition performance than polarimetricfeatures. Bai et al. found that the polarimetric features demonstrated poorer performance than
texture features in terms of distinguishing between damage categories.(16) Li et al. introduced
texture parameters of different statistical models to address the problem of confusing collapsed
and oriented buildings.(17) Zhai et al. proposed the precision-weighted multifeature fusion(PWMF) method to fuse four texture features for collapsed building extraction from PolSAR
images.(18) Chen et al. proposed a statistical texture feature G0-para arising from the G0
distribution of SAR images to distinguish buildings in complex urban areas after a disaster.(19) In
recent years, because of the rapid development of deep learning technology(20) and its highprecision recognition advantages,(21) many scholars are using the convolutional neural network(CNN) algorithm to recognize building earthquake damage information.(22,23)
In post-earthquake SAR images, the walls of many damaged buildings collapse, resulting in
the destruction of the dihedral structures formed by the walls and the ground, making it
impossible to attain the double-bounce scattering characteristics of SAR images with a highscattering intensity, causing volume scattering to be dominant over double-bounce scattering.
This results in the scattering intensity of collapsed buildings being lower than that of standing
buildings. However, a particular phenomenon that some standing buildings are not arranged
parallel to the SAR flight path exists in SAR images; such buildings are called obliquely oriented
buildings. With the rotation of the polarization basis of obliquely oriented buildings, thesebuildings are strongly depolarized, and volume scattering is their predominant scattering
mechanism, giving a low scattering intensity. Therefore, obliquely oriented and collapsedbuildings have similar scattering mechanisms and scattering intensities, so serious mutual
confusion exists in SAR images, leading to the overestimation of damaged buildings. To solve
this problem, we propose a novel frequency-domain texture feature based on Fourier transform,i.e., the sector texture feature of the Fourier amplitude spectrum (STFFAS). In addition,
STFFAS is used to design the building earthquake damage recognition scheme. Experimental
results showed that the earthquake damage recognition accuracy can be significantly improved

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by using STFFAS proposed in this paper. Our research design and experiment will be introduced
in detail below.

2.

Methods

2.1

Polarimetric decomposition method

Different scattering components can be obtained by polarimetric decomposition. In postearthquake PolSAR images, buildings in different states correspond to different scattering
components, e.g., some standing buildings that are parallel to the SAR flight path are called
parallel standing buildings, and their dominant scattering component is the double-bounce
scattering component; therefore, the earthquake damage recognition results of such buildings inearthquake-stricken areas can be directly obtained by polarimetric decomposition. In our work,
the buildings that are undamaged and whose dominant scattering component is the volume
scattering component are regarded as obliquely oriented buildings. Both collapsed buildings andstanding obliquely oriented buildings in earthquake-stricken areas are dominated by the volume
scattering component. In our research, we have conducted the polarimetric decomposition of
PolSAR data using the improved Yamaguchi four-component decomposition (Y4R) method torecognize standing buildings (including parallel standing and obliquely oriented buildings) in amore complete manner. The Y4R method was proposed by Yamaguchi et al. as an improved
version of the traditional Yamaguchi four-component decomposition model (Y4O) to increase
the building recognition accuracy and recognize obliquely oriented buildings as comprehensively
and accurately as possible (for more details on the Y4R method, see Ref. 24).
2.2

STFFAS

Volume scattering components obtained by polarimetric decomposition correspond to
obliquely oriented and collapsed buildings. Although they have similar scattering mechanisms
and scattering intensities, they exhibit different texture characteristics in PolSAR images. The
strength of the Fourier amplitude spectrum of SAR images represents the rate of change inscattering intensity. Obliquely oriented buildings have relatively regular and fine textures, while
collapsed buildings have relatively irregular and coarse textures. The differences in the
scattering intensities and textures of the two types of buildings can be better reflected withfrequency-domain texture features. In our research scheme, STFFAS is applied to classify thetwo types of buildings in earthquake-stricken areas to improve the over-classification ofcollapsed buildings and suppress the overcorrection of obliquely oriented buildings. STFFAS
can well describe the difference in texture between obliquely oriented and collapsed buildings,
and accurately recognize the two types of buildings. As shown in Fig. 1, the STFFAS values of
obliquely oriented buildings are greater than those of collapsed buildings, indicating thatSTFFAS is adequate for recognizing the two types of buildings.

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Fig. 1. (Color online) Recognition performance of STFFAS for obliquely oriented and collapsed buildings. Blueand red points in the figure represent 1000 obliquely oriented building samples and 1000 collapsed building samples,
respectively.

The STFFAS calculation method is shown in Fig. 2. The total power image of PolSAR data
(termed the SPAN image) contains the intensity information of all polarimetric channels. The
intensity information in the SPAN image is the most abundant. Therefore, STFFAS of PolSAR
data is calculated with the SPAN image. SPAN is calculated as
SPAN = HH2 + HV2 + VH2 + VV2.

(1)

Traverse the whole-scene SPAN image with the calculation window, calculate the STFFAS
values of each calculation window, and obtain the STFFAS data of the PolSAR imagery. The
STFFAS calculation method is described below, taking the calculation procedure for STFFAS
values in one calculation window as an example.
WI is the SPAN image in the calculation window. There are seven steps in the calculation of
STFFAS.
Step 1. Perform a two-dimensional fast Fourier transform (FFT) on WI to obtain FWI (fordetails on FFT, refer to Ref. 25).
Step 2. Calculate the amplitude spectrum of FWI to obtain AFWI.
Step 3. As shown in Fig. 2, take the center point of the AFWI image as the origin of thecoordinates and divide the circle of 2π equally into n parts around the origin of thecoordinates, with a vertex angle of 2π/n radians at the origin coordinates of each equalpart. Calculate the sum of AFWI values of all pixels in the ith equal part to obtain
AFWI_Ai. Calculate the sum of the AFWI values of all pixels for each equal part in turnto obtain n AFWI_Ai values {AFWI_A1, AFWI_A2, AFWI_A3, …, AFWI_An}.
Step 4. Calculate the ADFT value of AFWI according to the fourth line of Eq. (2).
Step 5. As shown in Fig. 2, divide the inscribed circle of AFWI into m rings of the same widthby taking the center point of the AFWI image as the origin coordinates. Assuming thateach ring has a width of RW, calculate the sum of the AFWI values of all pixels in the jthring to obtain AFWI_R j. Calculate the sum of the AFWI values of all pixels in each ringin turn to obtain m AFWI_R j values {AFWI_R1, AFWI_R 2, AFWI_R3, …, AFWI_
R m}.

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Fig. 2.

(Color online) Schematic diagram of STFFAS calculation.

Step 6. Calculate the RDFT value of AFWI according to the fifth line of Eq. (2).
Step 7. Calculate the STFFAS value of WI according to the last line of Eq. (2).
STFFAS can be expressed as

WI ⊂ span image of PolSAR data
FWI = FFT(WI )=AFWI[real( FWI )]2 + [imag( FWI )]2

ADFT = std  mean ( AFWI_A1 ) , mean ( AFWI_A2 ) ,  , mean ( AFWI_An ) 
RDFT = mean std ( AFWI_R1 ) , std ( AFWI_R2 ) ,  , std ( AFWI_Rm ) 
STFFAS = 10 × lg ( RDFT + 3 × ADFT ) .

(2)

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Here, real and imag represent the real and imaginary parts of a complex number, respectively;mean, std, and lg represent the calculated mean value, variance, and logarithm to the base 10,
respectively.
2.3

Building earthquake damage information extraction procedure

As shown in Fig. 3, the building earthquake damage information extraction procedureincludes the following six steps:
Step 1.	
Perform a polarimetric decomposition of the PolSAR data by the Y4R method to extract
the double-bounce and volume scattering components.
Step 2.	Classify the objects to be recognized in the PolSAR image, whose dominant scattering
components correspond to the double-bounce scattering components, as the parallel
standing buildings.
Step 3.	
Extract the SPAN image from the PolSAR data according to Eq. (1).
Step 4.	
Set up an appropriate calculation window, traverse the entire SPAN image by the
STFFAS calculation method shown in Fig. 2 and Eq. (2), calculate the STFFAS values,
and obtain the entire STFFAS image of the PolSAR data.
Step 5.	
Choose an appropriate threshold value for STFFAS, and divide the volume scattering
components obtained by polarimetric decomposition into collapsed and obliquelyoriented buildings on the basis of the classification rule expressed by Eq. (3). From Fig.
1, the STFFAS values of the obliquely oriented buildings are found to be greater thanthose of the collapsed buildings. Therefore, the classification rule of STFFAS can be
expressed as follows.

Fig. 3.

(Color online) Procedure of extracting damaged and undamaged buildings.

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x ∈ volume_dominated buildings
if STFFAS ≤ εx ∈ collapsed buildings(3)

if STFFAS > εx ∈ obliquely oriented standing buildings
Here, ε indicates the threshold value for distinguishing between collapsed and obliquelyoriented buildings using STFFAS.
Step 6.	Merge the obliquely oriented and parallel standing buildings as undamaged buildings,
and define collapsed buildings as damaged buildings.

3.

Materials

The case study presented in this paper is based on the 4.14 Ms7.1 Yushu earthquake that
occurred in Yushu County, Qinghai Province, China, on April 14, 2010. The epicentre of thisearthquake was located at (33.1°N, 96.6°E). Yushu County is at a high altitude where the climate
is dry and cold. There is very little vegetation in the urban area. It is sparsely distributed andmostly low vegetation, which has little impact on the identification of buildings in theearthquake-stricken area, so the ground object of vegetation was ignored in this experiment. The
mountains surrounding the county were masked using the boundary .shp data of Yushu County,
and only earthquake damage information of buildings in the urban area was extracted.
The experimental data was the airborne high-resolution PolSAR image obtained by the
Chinese airborne SAR mapping system (SARMapper) one day after the earthquake. Additional
information about the PolSAR data used in this experiment is listed in Table 1. Figure 4 presents
the 8192 × 4384 pixel SPAN image of the PolSAR data used to calculate STFFAS. In thisexperiment, 25000 verification samples were selected for collapsed, obliquely oriented, andparallel standing buildings, using the Google Earth Map and being colored red, blue, and green
in Fig. 4, respectively.

4.

Results

Damaged and undamaged buildings were extracted from the PolSAR data used in theexperiment by the building earthquake damage recognition procedure in Fig. 3. STFFAS was
calculated by the method shown in Fig. 2. In this work, the calculation window of STFFAS wasTable 1
Specific information of PolSAR data used in our experiment.
Illumination Incidence
Date
Flight direction
direction
angle
15. April 2010
From right to left
Bottom
50°

Band
P

Flight altitude
Spatial resolution (m)(m)
10,0791 (range); 1 (azimuth)

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Fig. 4. (Color online) SPAN image of Yushu County marked with the three types of building samples in theearthquake-stricken area.

set to be a square to simplify the experimental calculation. It can be seen from Fig. 5 that theoverall accuracy of classification of collapsed buildings, obliquely oriented buildings, andparallel standing buildings is the highest when calculating STFFAS using Eq. (2) with thecalculation window size of 57 pixels × 57 pixels. As shown in Fig. 6, during the calculation of
ADFT shown in Fig. 2, when the number n of equally divided angular zones in the circle of 2π in
the AFWI image is 36, that is, when the vertex angle of each equal angular zone is π/18 radians,
the overall classification accuracy for the three kinds of buildings is the highest. From Fig. 7, it isseen that during the calculation of RDFT shown in Fig. 2, when the ring width RW is set to 5
pixels, the classification of the three types of buildings has the highest overall accuracy. The
number of rings with equal ring width into which the inscribed circle of AFWI is divided isequal to window size/(2 × RW), that is, m = window size/(2 × RW). As can be seen from Fig. 1,
there is a clear dividing line between the STFFAS values of the collapsed buildings and those of
the obliquely oriented buildings at the STFFAS value of −8.22. The curve shown in Fig. 8 further
shows that when the STFFAS value is equal to −8.22, the overall classification accuracy of thethree kinds of buildings is the highest. Therefore, in our experiment, when classifying collapsedbuildings and obliquely oriented buildings using STFFAS according to Eq. (3), the classificationthreshold ε is set to −8.22.
The extraction results of building earthquake damage information in this experiment are
shown in Fig. 9 for only the buildings with earthquake damage in the urban area after masking
the mountains around the urban area using the urban boundary data of Yushu County. With theidentified samples of collapsed buildings, obliquely oriented buildings, and parallel standing
buildings marked in Fig. 4, the accuracy of the experiment results on building earthquake
damage recognition shown in Fig. 9 is evaluated, and the confusion matrix for the accuracy
assessment of our experimental results is shown as Table 2. Table 2 shows that the total numberof diagonal samples where experimental results are consistent with the reference samples
accounted for 81.3% of the total samples. Therefore, the overall correct recognition accuracy of
the three kinds of buildings in the research area using our proposed method reached 81.3%.

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Fig. 5.

(Color online) Selection of STFFAS calculation window.

Fig. 6. (Color online) Decision for the number of angular zones with same vertex angle in the circle of 2π of AFWI
shown in Fig. 2.

Fig. 7.
Fig. 2.

(Color online) Selection for the number of rings with same width in the inscribed circle of AFWI shown inSensors and Materials, Vol. 35, No. 10 (2023)

Fig. 8.

Fig. 9.

3773(Color online) Decision for the threshold value of STFFAS.

(Color online) Extraction results of the three post-earthquake building types in Yushu County.

Table 2
Confusion matrix for accuracy assessment of recognition results of three types of buildings.
ExperimentObliquely oriented buildings
Collapsed buildings
Parallel standing buildings(No. of samples)
Reference
Collapsed buildings
20266
4734
0
Obliquely oriented
7456
17544
0
buildings
Parallel standing buildings
1209
625
23166
Overall accuracy: 81.30%

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Discussion

There are some recognized collapsed buildings with an orderly and regular distribution at theimage boundary in the upper right corner of Fig. 9. This is because the SPAN image needs to be
replenished to adapt to the size of the calculation window when calculating STFFAS, which is
known as the boundary supplement effect. As seen in Table 2, when using the proposed method
to extract building earthquake damage information, the producer’s accuracy (PA) of damagedbuildings, which is the correct recognition rate of collapsed buildings, is 81.06%; and the PA ofundamaged buildings, which is the correct recognition rate of obliquely oriented and parallel
standing buildings, reaches 81.42%. That is, the PA of both damaged and undamaged buildings
is above 80%, showing that the recognition of damaged and undamaged buildings is relatively
balanced without an underestimation of undamaged buildings caused by the overestimation ofdamaged buildings or vice versa. Therefore, the recognition results of the proposed method arereliable for post-earthquake building damage assessment and support safety, because theoverrecognition of damaged buildings will lead to a waste of rescue resources, while theunderrecognition of damaged buildings will delay the rescue of buried people, increasing thedanger to them.
On the basis of the correct recognition rate of obliquely oriented buildings, 70.18% of theobliquely oriented buildings were separated from the volume scattering components. If suchobliquely oriented buildings are misclassified as collapsed buildings, much manpower andmaterial resources will be misdirected. By the Y4O method, all ground objects whose dominant
scattering component is the volume scattering components will be classified as collapsedbuildings. Although this method can achieve a recognition rate of 100% for collapsed buildings,
t he cor rect recog n it ion r ate of u nd a maged bu ild i ngs is on ly 46.33% , i.e.
23166/(25000 + 25000) ≈ 46.33%. Therefore, when using the Y4O method to recognize building
earthquake damage, the inaccurate classification of the volume scattering components willcause an immoderate sacrifice of the correct recognition rate of undamaged buildings in
exchange for the correct recognition rate of collapsed buildings.
Most of the misclassified collapsed and obliquely oriented buildings appear at the junction or
boundary of two ground objects owing to the square calculation window. A calculation window
may contain multiple ground objects and many rows and columns. The junction of different
objects is often only 1 or 2 pixels wide. Then, the calculation window will have a smoothing
effect at this boundary, so the two different objects at the junction will be recognized as one
object. Parallel standing buildings are misclassified as collapsed and obliquely oriented buildings
mainly because some roof areas and transition zones between overlaying and shadow have lower
scattering intensities. As a result, the parallel standing buildings with weaker scattering intensity
are misclassified as collapsed and obliquely oriented buildings. In addition, errors in the manual
selection of verification samples may lead to the misclassification of the three types of buildings,
e.g., other ground object samples that do not belong to the verification sample category may be
sporadically distributed in a complete verification sample zone. In other cases, the edges of
selected building samples may be prone to mixing with other ground objects.

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Since the STFFAS values of the obliquely oriented buildings are greater than those of thecollapsed buildings, the larger the STFFAS segmentation threshold value is, the more collapsedbuildings are recognized and the more collapsed buildings are misrecognized as obliquelyoriented buildings. This will result in an overestimation of collapsed buildings and anunderestimation of standing buildings. Conversely, the smaller the STFFAS segmentationthreshold value is, the fewer collapsed buildings are recognized and the easier it becomes fordamaged buildings to not be recognized, which may delay rescue operations and cause greatrisks to emergency rescue. Therefore, the segmentation threshold value of STFFAS for obliquelyoriented and collapsed buildings cannot be set too small to obtain a high recognition accuracy ofobliquely oriented buildings. It should be appropriately set on the premise that the correct
recognition rate of collapsed buildings is not less than 80%, so as to ensure that the correct
recognition rate of obliquely oriented buildings will be above 80%.

6.

Conclusions

To solve the problem of the overclassification of collapsed buildings when using the Y4O
method, in this paper, we proposed a parameter called STFFAS that can describe the texture
features for the amplitude spectrum of the frequency domain from Fourier transform. STFFAS
proposed herein can be used to better distinguish between collapsed and obliquely oriented
buildings, which present similar scattering intensities and are dominated by the same scattering
component in the SAR image. STFFAS well differentiates the spatial textures of collapsed andobliquely oriented buildings on the basis of the frequency-domain properties. Therefore, we
proposed to use STFFAS to process a large number of obliquely oriented buildings mixed
amongst the collapsed buildings recognized by the Y4R method in order to improve therecognition accuracy of building earthquake damage. The STFFAS image corresponding to the
SPAN image of PolSAR data was calculated. The volume scattering components obtained fromthe PolSAR data by the Y4R method were segmented using the STFFAS values and further
divided into collapsed and obliquely oriented buildings. The overrecognition of disasters bydirectly classifying all the volume scattering components obtained by the Y4O method into
collapsed buildings has been significantly alleviated. The experimental results also show that the
proposed method for extracting building earthquake damage information can better recognize
the damaged and undamaged buildings in the earthquake-stricken area, and hence attain arelatively high recognition accuracy of building earthquake damage. 70.18% of obliquelyoriented buildings were successfully separated from the collapsed buildings recognized by theY4R method, which has significantly improved the extraction accuracy of building earthquake
damage information. Because of limitations in our data, no further experimental data wereavailable for our experiment in this study. We will further verify the robustness of the proposed
method when other relevant earthquake data are obtained. In addition, we will conduct in-depth
research on the selection of segmentation thresholds to reduce artificial errors.

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Acknowledgments
This work was supported by the Gansu Earthquake Administration Innovation Team Special
Fund, grant number 2019TD-01-02; the National Natural Science Foundation of China, grant
number 42371404 and 41601479; the Gansu Province Science and Technology Program, grant
number 22JR5RA822; the Key Talent Project of Gansu Province, grant number 11276679015;
the Basic Research Project of Institute of Earthquake Forecasting, China Earthquake
Administration, grant number 2021IESLZ4; the State Scholarship Fund of China ScholarshipCouncil (CSC); and the Science for Earthquake Resilience of China Earthquake Administration,
grant number XH18049.

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