# Change Detection from SAR Images Based on Deformable Residual Convolutional
Neural Networks

Junjie Wang  Ocean University of China<EMAIL_ADDRESS>,  Feng
Gao  Ocean University of China<EMAIL_ADDRESS>and  Junyu Dong  Ocean
University of China<EMAIL_ADDRESS>
(2021)

###### Abstract.

Convolutional neural networks (CNN) have made great progress for synthetic
aperture radar (SAR) images change detection. However, sampling locations of
traditional convolutional kernels are fixed and cannot be changed according to
the actual structure of the SAR images. Besides, objects may appear with
different sizes in natural scenes, which requires the network to have stronger
multi-scale representation ability. In this paper, a novel Deformable Residual
Convolutional Neural Network (DRNet) is designed for SAR images change
detection. First, the proposed DRNet introduces the deformable convolutional
sampling locations, and the shape of convolutional kernel can be adaptively
adjusted according to the actual structure of ground objects. To create the
deformable sampling locations, 2-D offsets are calculated for each pixel
according to the spatial information of the input images. Then the sampling
location of pixels can adaptively reflect the spatial structure of the input
images. Moreover, we proposed a novel pooling module replacing the vanilla
pooling to utilize multi-scale information effectively, by constructing
hierarchical residual-like connections within one pooling layer, which improve
the multi-scale representation ability at a granular level. Experimental
results on three real SAR datasets demonstrate the effectiveness of the
proposed DRNet.

Change detection, synthetic aperture radar image, deformable convolution,
residual pooling

††journalyear: 2021††copyright: acmcopyright††conference: ACM Multimedia Asia;
March 7–9, 2021; Virtual Event, Singapore††booktitle: ACM Multimedia Asia
(MMAsia ’20), March 7–9, 2021, Virtual Event, Singapore††price: 15.00††doi:
10.1145/3444685.3446320††isbn: 978-1-4503-8308-0/21/03††ccs: Computing
methodologies Scene understanding††ccs: Computing methodologies Scene anomaly
detection

##  1\. Introduction

Remote sensing image change detection aims to accurately detect the changed
information of the same geographical area by analyzing two images captured at
different times. It plays an essential role in many applications, such as
target detection (Bai10, ), disaster monitoring (Martino07, ), and natural
resource supervision (Bruzzone97, ).

Benefiting from the rapid development of the earth observation programs, more
and more multi-temporal SAR images are available. These SAR images are
produced by an active system that sends a signal to the ground and receives
the reflected signal. Since the active sensors are capable of observing the
earth all-weather and all-time, SAR images have become the ideal data source
for change detection.

In the past few years, many SAR images change detection methods are proposed.
These methods can be broadly divided into two directions: supervised and
unsupervised (Chen19, ). The supervised methods generally perform better since
more prior information is taken into account. However, supervised methods
require a large volume of high-quality labeled samples, and sample collection
is time-consuming. Therefore, it is nearly impossible to perform supervised
change detection in practice. The unsupervised methods are more preferable.
Therefore, we mainly focus on unsupervised change detection in this paper.

Existing unsupervised change detection methods are generally comprised of
three steps: 1) preprocessing, 2) difference image (DI) generation, 3) DI
classification. In the first step, geometric correction and registration are
usually involved and play an important role. In the second step, the ratio-
based method (Bazi05, ) is commonly used to generate a DI. Many efficient
operators are proposed, such as the log-ratio operator (Dekker98, ), Gauss-
ratio operator (Hou14, ), and neighbor-based-ratio operator (Gong12, ). In the
step of DI classification, many methods based on convolutional neural networks
(CNNs) have been proposed. Liu _et al._ (Liu18, ) presented a change detection
framework based on convolutional coupling network. Mou _et al._ (Mou19, )
proposed a change detection framework which combines CNNs and recurrent neural
network into one end-to-end network. Gao _et al._ (Gao19, ) detected changed
information from sea ice SAR images by transferred deep CNNs. Liu _et al._
(Liu19, ) presented a local restricted CNN framework for SAR change detection,
in which the original CNNs are improved with the local spatial constraint.

However, there are some shortcomings in these methods. On the one hand, these
methods use a fixed grid in the sampling location of the convolutional
kernels, and the spatial features of the complex structure are not fully
exploited. On the other hand, these methods do not fully learn the information
of objects with different sizes, and the multi-scale representation ability by
utilizing features with different resolutions is insufficient.

Figure 1. Illustration of the architecture of the DRNet

To tackle the aforementioned limitations of existing CNN-based change
detection methods, we present a Deformable Residual Convolutional Neural
Network (DRNet) to detect the changed information from multitemporal SAR
images. On the one hand, we introduce deformable convolutional sampling
locations (Dai17, ) to extract features of different shapes of ground objects.
The deformable convolutional sampling locations can be obtained by calculating
the 2-D offsets of each pixel of the input image. As illustrated in Fig. 2.
The offsets are learned from the preceding feature maps via additional
convolutional layers. Thus, the deformation is conditioned on the input
features in a local, dense, and adaptive manner. On the other hand, we design
a novel pooling module named residual pooling, which extracts multi-scale
features on a granular level. We replace the pooling kernel of $n$ channels
with a set of kernel group, each with $w$ channels ($n=s\times w$). Then these
features after pooling are connected in a hierarchical residual-like style to
increase the number of scales that output features can represent. Extensive
experiments are conducted on three SAR datasets to validate the effectiveness
of the proposed DRNet. The results demonstrate that the proposed DRNet can
well adapt to the complex spatial structure of SAR images and achieves better
performance compared with the state-of-the-art methods.

Figure 2. Illustration of the sampling locations in $3\times 3$ standard and
deformable convolutions. (a) Regular sampling grid of standard convolution.
(b) Deformable sampling locations.

In summary, the main contributions of the proposed DRNet are as follows:

  * •

We introduce deformable convolution into SAR change detection which can
adaptively change the sampling location according to the complex spatial
structures, and thus improve the performance of change detection.

  * •

To explore the multi-scale information of the ground objects, we design a
novel pooling module-residual pooling, which is simple and flexible variants
of pooling for boosting the multi-scale representation ability of CNNs. It
utilizes a set of smaller pooling kernels with $w$ channels to replace the
vanilla pooling with $n$ channels, then it connects these kernels in a
hierarchical residual-like style, which increases the number of scales that
output features can represent.

  * •

We conducted extensive comparative experiments on three SAR datasets to
validate the effectiveness of DRNet. The results demonstrate that the proposed
DRNet can represent multi-scale information better and adaptively adjust the
convolution shape according to the structure of objects.

##  2\. Methodology

In this paper, we devote to address the problem of change detection from
multitemporal SAR images. Given two coregistered SAR images $I_{1}$ and
$I_{2}$ captured in the same geographical area at different times, we aim to
generate a change map which shows the change information between two images.
It is generally treated as a binary classification task. In the generated
change map, the change pixels are marked as “1”, and unchanged pixels are
marked as “0”.

The proposed change detection method has two advantages compared to
traditional CNN-based methods. First, we introduce the deformable convolution
to capture the complex spatial information of SAR images. Besides, we design a
novel pooling module - residual pooling to fuse multi-scale information in the
pooling layer. In this section, we first describe the implementation of
deformable convolution, then present the detailed structure of residual
pooling.

###  2.1. Deformable Convolution

The CNN-based SAR change detection methods employ a stack of layers to extract
features from the input images and identify the change information (Cheng17,
). However, the convolutional kernels of regular CNNs can hardly adapt to the
spatial structure of input images, since the sampling locations of the
convolutions are fixed grids ($N\times N$ pixels).

To address this issue, we introduce the deformable convolution (Dai17, ) to
adjust the sampling location. In the network, we add two steps before regular
convolution: offset field generation and deformable feature map generation. As
is shown in Fig. 3, the offsets are learned from the preceding feature maps,
via additional convolutional layers. Given a pixel in an input feature map
with location $(x,y)$ and value $p(x,y)$, it corresponds to two values $\Delta
x$ and $\Delta y$ in the offset fields. A deformable feature map fuses the
neighboring similar pixels. The new pixel value can be denoted by:

(1) $\begin{split}p_{\textrm{new}}(x,y)=p(\min(\max(0,x+\Delta x),N-1),~{}\\\
\min(\max(0,y+\Delta y),N-1)).\end{split}$

The weight of the convolutional filters for generating offset fields are
trained based on spatial features to enable the sampling locations to be
transferred to the neighboring similar pixels. Finally, regular convolutions
are employed on the deformable feature maps, and the output can be obtained as
follows:

(2) ${y(x,y)}={\sum_{s_{w}}w_{ij}}\cdot p_{new}(x_{ij},y_{ij}),$

where $w_{i,j}$ represents the corresponding weight of the kernel and $s_{w}$
enumerates all locations of the kernel.

Figure 3. Illustration of $3\times 3$ deformable convolutions.

###  2.2. Residual pooling

Multi-scale feature representation is of great importance for numerous vision
tasks. Recent advances in backbone convolutional neural networks (CNNs)
continually demonstrate that stronger multi-scale representation ability
results in better visual understanding. However, the common solution of multi-
scale feature extraction includes making CNNs deeper or building multiple
branches, which increase the complexity and the amount of parameter of model,
requiring more time and resources to optimize.

To avoid these inconveniences, Huang _et al._(Huang18, ) proposed stacked
pooling which focuses on the pooling module to extract multi-scale
information. It can capture the multi-scale responses by stacking multiple
pooling kernels. The advantage of using pooling layer to extract multi-scale
features is non-parametric and it makes the network as simple as possible.
However, in (Huang18, ), the features are extracted in a layer-wise manner. As
mentioned in (Xie, ), in addition to the dimension of width and depth, the
cardinality of features is also a concrete, measurable dimension that is of
central importance. Increasing cardinality is a more effective way of gaining
accuracy than making network deeper or wider. Inspired by this idea and Gao’s
work (Gao19_arxiv, ), we proposed residual pooling, improving the multi-scale
representation capability at a more granular level, which is orthogonal to
exiting methods that utilize layer-wise operations. Thus, the proposed
residual pooling can be easily plugged into most exiting CNN models replacing
the vanilla pooling.

To achieve this goal, we replace the max-pooling operator of $n$ channels with
$s$ feature map subsets, and each subset contain $w$ channels ($n=s\times w$).
As is shown in Fig. 4, these smaller pooling subsets are connected in a
hierarchical residual-like style to increase the number of scales that the
output features can represent. Specifically, we evenly divide input feature
maps into $s$ feature map subsets. denoted by $x_{i}$, where
$i\in(1,2,...,s)$. Each feature subset $x_{i}$ has the same spatial size but
$1/s$ number of channels compared with the input feature map. Each $x_{i}$ has
a corresponding $2\times 2$ max-pooling operator, denoted by $K_{i}(\cdot)$.
$y_{i}$ is used to denote the output of $K_{i}(\cdot)$. The feature subset
$x_{i}$ is added with the output of $K_{i-1}(\cdot)$ and then fed into
$K_{i}(\cdot)$. Therefore, $y_{i}$ can be defined by:

(3) $y_{i}=\left\\{\begin{array}[]{rcl}K_{i}(x_{i})&&{i=1;}\\\
K_{i}(x_{i}+y_{i-1})&&{2\leq i\leq s;}\\\ \end{array}\right.$

Figure 4. Comparison among the conventional pooling, stacked pooling, and
residual pooling. The 2 $\times$ 2 max-pooling operator is taken as an
example.

##  3\. Experimental Results and Analysis

In this section, we first describe the datasets and the evaluation criteria
used in our experiments. Then, some investigation of several important
parameters on the change detection performance is presented. After that, we
make some ablation experiments to verify the effect of deformable convolution
and residual pooling. Finally, the proposed method is compared with several
state-of-the-art methods.

###  3.1. Dataset Description and Evaluation Criteria

Figure 5. The datasets used in our experiment.

In Fig. 5, we show the three datasets used in our experiments. Figures (a) and
(b) represent SAR images captured at different times, and (c) represents the
ground truth image. The first line of Fig. 5 is Florence dataset, which is
captured over the city of Florence, Italy. It illustrate an area with a varied
and complex morphology. The second line represents the the Sulzberger dataset.
It show the process of the sea ice breakup. The last dataset is the Seoul
dataset, which is selected from two SAR images captured over Seoul, South
Korea.

The performance evaluation of change detection is a critical issue. We use
false positives (FP), false negatives (FN), overall error (OE), percentage of
correct classification (PCC) as indicators to measure the effectiveness of the
proposed method. The FP is the number of pixels that are unchanged class in
the ground truth image but wrongly classified as changed ones. The FN is the
number of pixels that are changed class in the ground truth image but wrongly
classified as unchanged ones. The OE is computed by OE $=$ FP $+$ FN. The PCC
is computed by $\textrm{PCC}=(1-\textrm{OE}/Nt)\times 100\%$, where $Nt$
represents the total pixels in the ground truth image.

###  3.2. Analysis of the crucial parameters of DRNet

####  3.2.1. Analysis of different number of training samples

The number of training samples is an essential parameter in the SAR image
change detection, since most deep learning-based methods need a large number
of samples to optimize parameters. In this subsection, we show the
relationship between PCC and training samples number on three datasets.

Figure 6. Relationship between PCC and the crucial parameters.

To verify the validity of the model, we randomly selected 2%, 4%, 6%, 8%, and
10% pixels as training samples, respectively. Fig. 6 (a) shows the
quantitative analysis result of the relationship between PCC and the number of
training samples. From these curves, we can see that there is a sharp increase
in the PCC value when the number of training samples from 2% to 6%. After
that, the PCC value tends to become stable even the number of training samples
becomes larger. Thus, we select 6% pixels as training samples. Compared with
other methods (Gao19_rs, )(Gao19_grsl, ), DRNet needs fewer samples because
the structure of the model is relatively simple, and the proposed residual
pooling does not introduce additional parameters.

####  3.2.2. Analysis of the parameter $s$

The parameter $s$ denotes the number of subsets after the feature maps are
sent to the residual pooling, and it is a critical parameter in residual
pooling. We evaluate the classification performance by taking $s$=1, 2, 3, 4,
and 5, respectively. The PCC value is investigated as the validation criterion
on three datasets.

From Fig. 6 (b), we can observe that when $s=4$, the proposed DRNet obtains
the best results. It indicates that the multi-scale information can be
extracted effectively by dividing the feature maps into groups for pooling of
different scales, leading to the improvement of performance. It should be
noted that when $s=1$, the residual pooling is equal to the vanilla pooling.
By comparing the results of $s=1$ and $s=4$, we can confirm that the residual
pooling can replace vanilla pooling flexibly, and obtain some improvement in
the field of change detection.

###  3.3. Ablation Studies

We further discuss and validate the effectiveness of the deformable
convolution and residual pooling on three datasets. It should be noted that in
order to make a fair comparison, we have designed a $Basic\ network$ with the
same structure as DRNet, which uses regular convolution and vanilla pooling
replacing deformable convolution and residual pooling. The results are shown
in Table 1.

Table 1. Ablation experiments on three datasets. # | Basic network |  Deformable convolution | Stacked pooling | Residual pooling | Florence dataset | Sulzberger dataset | Seoul dataset
---|---|---|---|---|---|---|---
1 | ✓ | ✗ | ✗ | ✗ | 98.26 | 96.95 | 97.84
2 | ✓ | ✓ | ✗ | ✗ | 98.42 | 97.10 | 98.09
3 | ✓ | ✓ | ✓ | ✗ | 98.56 | 97.23 | 98.18
4 | ✓ | ✓ | ✗ | ✓ | 98.75 | 97.40 | 98.37
5 | ✓ | ✗ | ✓ | ✗ | 98.33 | 97.05 | 97.91
6 | ✓ | ✗ | ✗ | ✓ | 98.43 | 97.13 | 98.04

As reported in Table 1, although the $Basic\ network$ is a deep learning-based
model, due to the fixed sampling locations of traditional convolution and the
limitation of the receptive field, the performance of $Basic\ network$ is not
satisfying. When the deformable convolution is introduced into the network, it
can adaptively adjust the shape of convolution, reflecting the complex
structure more effectively, resulting in the improvement of performance (#1 vs
#2). Moreover, the advances in backbone CNN caused by stronger multi-scale
representation ability demonstrated a trend toward more effective and
efficient multi-scale representations. There, we proposed residual pooling,
which does not introduce extra parameters and can easily take place of the
vanilla pooling layer. In order to verify its effectiveness, we compare it
with vanilla pooling (#1 vs #6). Through the experiment, we can know that
perceiving information from different scales is essential which further
indicates the advantage of residual pooling compared with vanilla pooling. It
also should be noted that compared with residual pooling, when we introduce
stacked pooling (#5), the experiment results also are improved, but not as
good as residual pooling. The reason for this phenomenon is that feature maps
contain different granularity features, and unified pooling will lead to the
loss of information. After grouping feature maps, residual pooling can not
only expand the receptive field, but also better explore the relevance of
information between different feature maps. The proposed DRNet which combines
the deformable convolution and residual pooling has received the best result
on three datasets, not only showing their respective effectiveness, but also
showing the complementary effects between them (#4).

###  3.4. Results on the Three Datasets

In order to verify the effectiveness of the proposed DRNet, we compare it with
six closely related methods: PCAKM (Celik09, ), NBRELM(Gao16, ), GaborPCANet
(GaoGRSL16, ), MLFN(Gao19_grsl, ), LR-CNN (Liu19, ), and DBN (Gong16, ). In
PCAKM, the contextual information is taken into account by principal component
analysis (PCA), and the extracted features are clustered by the k-means
algorithm. NBRELM utilizes NR operator to obtain some pixels belonging to
changed or unchanged class, then these pixels are put into ELM to train a
model. GaborPCANet is a simplified deep learning model which is comprised of
two PCA convolutional layer, binary hashing layer and block-wise histogram
generation layer. In MLFN, a large dataset is used to train a model, and the
deep knowledge can be transferred to change detection. LR-CNN is a
convolutional neural network with local spatial restrictions on the output
layer. In DBN, a deep belief network is employed to complete the SAR change
detection task, which produces a change detection map directly from two images
with a trained deep neural network. In order to more intuitively and
adequately reflect the results of various methods, both visual and
quantitative analyses are made in our experiments. For visual analysis, the
change maps generated by different methods are exhibited in figure form. For
quantitative analysis, the change maps are exhibited in tabular form.

Figure 7. Visualized results of different change detection methods on three datasets. (a) Ground truth image. (b) Result by PCAKM (Celik09, ). (c) Result by NBRELM (Gao16, ). (d) Result by GaborPCANet (GaoGRSL16, ). (e) Result by MLFN (Gao19_grsl, ). (f) Result by LR-CNN (Liu19, ). (g) Result by DBN (Gong16, ). (h) Result by the proposed DRNet. Table 2. Change detection results of different methods on the three datasets. Method | Florence | Sulzberger | Seoul
---|---|---|---
FP | FN | OE | PCC(%) | FP | FN | OE | PCC(%) | FP | FN | OE | PCC(%)
PCAKM (Celik09, ) | 1307 | 663 | 1970 | 96.99 | 2368 | 203 | 2571 | 96.08 | 1013 | 998 | 2011 | 96.93
NBRELM (Gao16, ) | 1186 | 598 | 1784 | 97.28 | 1860 | 579 | 2439 | 96.28 | 871 | 963 | 1834 | 97.20
GaborPCANet (GaoGRSL16, ) | 1062 | 579 | 1642 | 97.50 | 1410 | 783 | 2193 | 96.65 | 547 | 926 | 1473 | 97.75
MLFN (Gao19_grsl, ) | 851 | 406 | 1257 | 98.08 | 1254 | 819 | 2073 | 96.84 | 492 | 875 | 1367 | 97.91
LR-CNN (Liu19, ) | 793 | 364 | 1157 | 98.23 | 1198 | 680 | 1878 | 97.13 | 420 | 854 | 1274 | 98.06
DBN (Gong16, ) | 725 | 330 | 1055 | 98.39 | 632 | 1242 | 1874 | 97.14 | 362 | 869 | 1231 | 98.12
Proposed DRNet | 603 | 215 | 818 | 98.75 | 621 | 1183 | 1704 | 97.40 | 343 | 725 | 1068 | 98.37

Fig. 7 illustrates the final change maps of different methods on the three
datasets, the first row shows the results of different methods on Florence
dataset, and the next two rows are Sulzberger and Seoul datasets. Table 2
lists the corresponding evaluation criteria of different methods. From the
visual comparison, it can be observed that just like the area marked by the
red circle, there are some noisy white spots in the results generated by
PCAKM, NBRELM, GaborPCANet, MLFN and LR-CNN, which corresponds to the higher
FP value in the Table 2. What’s more, it can be seen that the PCC and OE value
of deep learning-based methods (MLFN, LR-CNN and DBN) is generally better. It
means that deep learning can enhance model comprehension better, thereby
reducing detection errors. Besides, from Table 2, we can observe that the
proposed DRNet has the minimum FP and FN value, leading to that the proposed
DRNet can provide the most similar results to the ground-truth change map
compared with other methods. On three datasets, the OE and PCC values of DRNet
performs better than other methods, the PCC values have increased by at least
0.36%, 0.26% and 0.25%, respectively. It further proves that the proposed
DRNet can adapt to the complex structure of the dataset and improve the multi-
scale representation ability.

##  4\. Conclusion

In this paper, a deformable residual convolutional neural network (DRNet) is
proposed for the task of SAR image change detection. Compared to existing CNN-
based methods, the DRNet makes improvements in two aspects. First, we
introduce deformable convolution to extract more effective spatial features
according to the complex spatial structure of the ground objects. Besides, we
design multi-scale residual pooling to utilize multi-scale information at a
granular level. Compared with other methods using multi-scale information, we
focus on the pooling layer, which not only not introduces additional
parameters, but also can easily take place of the vanilla pooling layer in the
implementation. Compared to vanilla pooling, the residual pooling can not only
expand the receptive field, but also capture different granularity features.
Experimental results on three real SAR datasets demonstrate that the proposed
DRNet has better performance than some state-of-the-art methods in terms of
both the visual quality of the change map and evaluation criteria.

## Acknowledgement

The authors would like to thank the anonymous reviewers for their helpful and
insightful comments. This work is supported by the National Key Research and
Development Program of China (No. 2018AAA0100602), the National Natural
Science Foundation of China (No. U1706218), and the Key Research and
Development Program of Shandong Province (No. 2019GHY112048).

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