=Paper=
{{Paper
|id=Vol-3207/paper2
|storemode=property
|title=A Deep Feature Retrieved Network for Bitemporal Remote Sensing Image Change Detection
|pdfUrl=https://ceur-ws.org/Vol-3207/paper2.pdf
|volume=Vol-3207
|authors=Shizhen Chang,Michael Kopp,Pedram Ghamisi
|dblpUrl=https://dblp.org/rec/conf/cdceo/Chang0G22
}}
==A Deep Feature Retrieved Network for Bitemporal Remote Sensing Image Change Detection==
https://ceur-ws.org/Vol-3207/paper2.pdf
A Deep Feature Retrieved Network for Bitemporal Remote
Sensing Image Change Detection
Shizhen Chang1 , Michael Kopp1 and Pedram Ghamisi1,2
1
Institute of Advanced Research in Artificial Intelligence (IARAI), 1030 Vienna, Austria
2
Helmholtz-Zentrum Dresden-Rossendorf, Helmholtz Institute Freiberg for Resource Technology, Machine Learning Group, 09599 Freiberg,
Germany
Abstract
The task of bitemporal change detection aims to identify the surface changes of specific scenes at two different points in
time. In recent years, we have increasingly witnessed the success of deep learning in a variety of applications in remote
sensing, including change detection and monitoring. In this paper, a novel deep feature retrieval neural network architecture
for change detection is proposed that uses a trainable associative memory component to exploit potential similarities and
connections of the deep features between image pairs. A key ingredient in our novel architecture is the use of a continuous
modern Hopfield network component. The proposed method beats the current state-of-the-art on the well-known LEVIR-CD
data set. The codes of this work will soon be available online (https://github.com/ShizhenChang).
Keywords
Remote sensing, change detection, modern Hopfield network, deep learning, Siamese network, convolutional neural network.
1. Introduction gories: early fusion [7, 8] and late fusion [9] networks.
The early-fusion networks first concatenate multitem-
With the rapid development of technologies for Earth poral images into a unified data cube, and then, the pa-
observation, an ever-growing amount of very high reso- rameters are hierarchically fine-tuned. The late-fusion
lution (VHR) remote sensing data has become available networks usually learn single-temporal features individ-
for geographical analysis and image processing [1]. VHR ually and share the parameters by using a Siamese net-
images can provide detailed information about land sur- work. Compared to early-fusion networks, late-fusion
faces, and images collected at different time epochs from methods can better utilize the features of the inputs and
the scene are able to record changes regularly. There- return clearer contours of the change objects. However,
fore, as one of the most important remote sensing tasks, the features of shallower layers may not be sufficiently
change detection has been widely applied in many areas learned and utilized due to the gradient vanishing prob-
of land-use and land-cover analysis, such as environmen- lem. Therefore, learning information from both shallow
tal monitoring, urban growth, deforestation assessment, and deep layers are very important to effectively detect
shifting cultivation evaluation, and so on. changes using deep-learning-based approaches.
A variety of deep neural networks, such as the convo- In order to accurately extract features, deeper and more
lutional neural networks (CNNs) [2], autoencoders (AEs) complex CNN-based networks have been designed, that
[3], recurrent neural networks [4], generative adversar- include architecture components such as Long Short-
ial network (GAN) [5], and deep belief network (DBNs) Term Memory (LSTM) [10] and attention mechanisms
[6], have been successfully utilized for remote sensing (self-attention [11], spatial attention [12], and channel
change detection over the last few years. Among them, attention [8]). The successful combination of CNNs and
CNN-based methods can take full use of the spatial infor- other networks has shown that discriminative features
mation of VHR remote sensing images, thus, can better within the image pairs can be better extracted and the
extract high-level deep features and abstract semantic detection accuracy can be greatly improved. However,
contents to learn discriminative differences between the limited by the architecture of CNNs, as the high-level
periods. features are only related to the shallower layers through
Strategies that have been applied to extract deep fea- larger receptive fields, the global and temporal informa-
tures of the inputs, can be broadly divided into two cate- tion between the image pairs are still not sufficiently
CDCEO 2022: 2nd Workshop on Complex Data Challenges in Earth utilized.
Observation, July 25, 2022, Vienna, Austria To address this issue, we design a Hopfield pooling
Envelope-Open shizhen.chang@iarai.ac.at (S. Chang); michael.kopp@iarai.ac.at block to interactively retrieve the high-level concepts of
(M. Kopp); pedram.ghamisi@iarai.ac.at (P. Ghamisi) changes. This idea is inspired by the successful appli-
Orcid 0000-0002-9785-7937 (S. Chang); 0000-0002-1385-1109
cation of the modern Hopfield network for continuous
(M. Kopp); 0000-0003-1203-741X (P. Ghamisi)
Β© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License pattern retrieval [13]. Our assumption is that the seman-
Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings CEUR Workshop Proceedings (CEUR-WS.org)
http://ceur-ws.org
ISSN 1613-0073
tic information between the image pairs in deeper layers
Z Z Z
Y Y
R R
(a) (b) (c)
Figure 1: A brief illustration of three types of Hopfield layers for deep learning [13], where both the stored patterns Y and
the query patterns R can be obtained from the previous layers or the input or can be learned. The output Z are the retrieved
patterns for the queries, each being a linear combination of stored patterns lying in the convex hull of the simplex spanned by
the stored patterns. (a) This Hopfield layer associates two sets R and Y to propagate sets of vectors. (b) Layer Hopfield Pooling
layer performs a pooling operation to the set Y via learned queries. (c) The Hopfield layer learns a new set of stored patterns
based on the input R.
can be represented using a common matrix, i.e. a query, hull of the simplex spanned by the {π₯1 , ..., π₯π }, such the
that can be learned during the training process. We use following energy function is minimized:
this query to retrieve related semantic features between π
given images. These retrieved features reflect a com- 1 1
πΈ = βπ½ β1 log(β exp (π½π₯πβ€ π ))+π½ β1 log π + π β€ π + π 2 ,
mon spatio-temporal context and are used by subsequent π=1 2 2
layers in our network. Concretely, we incorporate a Hop-
field network block into a Siamese fully convolutional where π is the largest norm of the {π₯1 , ..., π₯π } in βπ . As
network (FCN) resulting in the design of our proposed shown in [13, 18], π πππ€ is defined by the following update
deep feature retrieved network (FrNet) for bitemporal rule:
remote sensing change detection. It should be noted that π πππ€ = π (π ; π , π½) = πsoftmax(π½π β€ π ), (1)
different from previous change detection models, both
semantic and temporal information can be fully consid- which will converge globally, almost always, to a local
ered; and it is our first attempt of using modern Hopfield minima of the energy function in essentially one update
networks in the remote sensing community. step. Moreover, equation (1) is closely related to the well
The rest of this article is organized as follows. Section known transformer attention mechanism, showing that
II briefly reviews continuous modern Hopfield networks. retrieval in modern Hopfield networks and transformer
Section III describes the proposed method. Experiments attention coincide [13, 18].
are conducted and discussed in Section IV. With changable structures in deeper networks (as
shown in Fig. 1), continuous modern Hopfield networks
have greater application prospects in deep learning. It
2. Continuous Modern Hopfield has been successfully applied to solving large scale multi-
Network instance learning tasks [19], to few- and zero-shot chem-
ical reaction template prediction [20], to creating new
Binary modern Hopfield networks are associative mem- reinforcement learning algorithms [21, 22], to improving
ories on binary data that can retrieve data of exponen- contrastive learning of joint image- and text embedding
tially many stored patterns [14, 15], this being the key representations [23] and to tabular data [24].
distinguishing feature to their classical binary counter- Inspired by continuous modern Hopfield networks,
parts [16, 17]. These binary modern Hopfield networks we design a Siamese Hopfield pooling layer and attempt
have been generalized to continuous modern Hopfield to capture deep feature differences for remote sensing
networks that, crucially, are differentiable and can thus bitemporal change detection.
be embedded in deep learning architectures trained by
gradient descent [13, 18]. Moreover, continuous modern
Hopfield networks retain the key ability to store exponen- 3. Deep Feature Retrieved
tially many patterns and they can furthermore retrieve Network for Change Detection
patterns in only one update step.
Given a matrix π of shape π Γ π formed of column 3.1. Overview
vectors {π₯1 , ..., π₯π } β βπ , a query pattern π, also a column
As shown in Fig. 2, the proposed deep feature retrieved
vector, seeks to retrieve the best pattern in the convex
network (FrNet) is a Siamese network that contains three
π€π€
π€π€
π€π€/2
π€π€/4
Reshape
T1 Image
β/32 βπ€π€/322 Γ 512
π€π€/32
Softmax
β/16 512 π€π€/8
β/8 π€π€/16
512
β/4 π€π€/8
π€π€/16
256
β/2 π€π€/4
β 128 π€π€/32
π€π€/2 Query β
Shared Weight β/2
64 2 Γ βπ€π€/322 C β/8
β/4
64 β/32
β/16
π€π€ 16
128
16 512 256 256 256 128 128 128 64 64 64 32 32 2
π€π€/2 Change Map
256
π€π€/4
Softmax
β
512
β/2 π€π€/8 : Backbone blocks
β/4 π€π€/16 512
β/8 Reshape : 1Γ1 convolution
β/16 β/32 βπ€π€/322 Γ 512
T2 Image
π€π€/32 : Up sample
: Matrices difference
: Matrix multiplication
C : Concatenation
Figure 2: Flowchart of the proposed FrNet.
parts: a feature extractor, a Hopfield pooling block, and that is related to changed objects from the bitemporal
a decoder. Bitemporal change detection can be viewed deep features?β. We design a Hopfield pooling block to
as a segmentation task for image pairs that record the pool the features of various channels into fewer channels,
same geographic information at different times. Since the and at the same time, attempt to interactively retrieve
shapes and sizes of changed objects vary a lot, deeper lay- semantic information during the period of changes using
ers of CNN-based approaches (e.g., U-Net and U-Net++) the Hopfield update rule.
can effectively extract semantic features and retain details Let us assume two temporal VHR images are denoted
with a larger receptive field. To extract useful informa- by ππ β β3ΓβΓπ€ , where π = {1, 2} represents the π-th time
tion from bitemporal images, a Siamese network with period and β and π€ are the height and width of the im-
consistent architectures and shared weights are utilized ages, respectively. Features obtained by the backbone
as the feature extractor in our implementation (shown Μ
are denoted as πΉπ β βπΓβΓπ€Μ , where π, β,Μ and π€Μ represent
with green blocks in Fig. 2). The VGG-16 [25] with Ima- the number of channels, height, and width of the feature,
geNet pretrained parameters is chosen as the backbone respectively. For the proposed VGG-16 feature extractor,
network. Then the spatial dimensions of deep features the channel size of πΉπ is 512, and the height and width of
are flattened and input into the Hopfield pooling block. the features are 1/32 of the original image.
The deep features of two periods are pooled and retrieved. In the Hopfield pooling block, the features are first
After that, we feed the concatenation of the bitemporal Μ Μ
reshaped into ββπ€Γπ of row-wise vectors. Then, for the
retrieved features and the feature differences from shal-
time 1 image, we introduce a trainable weight matrix
lower layers into the decoder and obtain the change map. Μ
The decoding modules are shown in the right part in ππ β βππ Γβπ€Μ to retrieve the related deep features of πΉ1
Fig. 2. related to the 2nd period. The output can be written as:
π1 = softmax(π½ππ πΉ1β€ )πΉ2 . (2)
3.2. Hopfield Pooling Block
The number of rows ππ in ππ is set to 2 in this paper
The Hopfield layer is proven to be capable of retrieving
which represents the change/unchange semantic infor-
key features of the input through one update. For the
mation we retrieved.
proposed bitemporal change detection task, the question
Similarly, the common weight matrix ππ is utilized to
is: βhow can we obtain the most typical information
retrieve πΉ2 related to the 1st period: Table 1
Quantitative Analysis of Different Networks on the LEVIR-CD
π2 = softmax(π½ππ πΉ2β€ )πΉ1 . (3) Data Set. The Best Values are shown in Bold
It should be noted that the retrieved output π1 and π2 Methods Pre (%) Rec (%) F1 (%) OA (%)
have the same size and contain both global and temporal FC-EF 61.86 96.05 75.25 96.78
information of the image pairs. FC-Siam-conc 67.87 97.53 80.04 97.52
We concatenate the retrieved outputs together: π = FC-Siam-diff 71.37 95.42 81.66 97.82
[π1 ; π2 ], restore their spatial dimensions, and feed them BIT 80.82 92.86 86.42 98.51
into a 1 Γ 1 2D convolutional layer with 16 filters to Base Model 85.24 92.26 88.61 98.79
generate a new feature map. After bilinear interpolation, FrNet 86.32 92.10 89.12 98.85
the features through the Hopfield pooling block is finally
derived:
π» = π (π(π β π + π)), (4) proposed FrNet, we also set a base model that consists of
where π and π represent the weight matrix and bias vec- the CNN backbone (VGG-16) and the decoder for com-
tor of the convolutional layers, β denotes the 2D convo- parison.
lutional operation, π(β
) denotes the batch normalization For the evaluation part, the precision (Pre), recall (Rec),
with ReLU activation, and π (β
) denotes bilinear interpo- F1 score, and overall accuracy (OA) are employed to quan-
lation with an upsampling rate of 2. titatively evaluate the performance of the studied meth-
ods. These metrics are calculated as follows:
ππ
4. Experiments πππ =
ππ + πΉπ
(5)
ππ
4.1. Data Set π
ππ = (6)
ππ + πΉπ
In the experimental part, the LEVIR-CD data set [26] is 2πππ β
π
ππ
πΉ1 = (7)
utilized to compare the change detection methods. The πππ + π
ππ
LEVIR-CD data set is composed of 637 VHR (0.5m/pixel) ππ + ππ
ππ΄ = (8)
Google Earth (GE) image pairs with the size of 1024Γ1024 ππ + ππ + πΉπ + πΉπ
pixels. These image pairs have been captured in differ- where TP (True Positive) represents the number of pix-
ent periods of 5 to 14 years and cover a total of 31,333 els of real changes that are correctly detected, FP (False
individual buildings for the task of building growth as- Positive) represents the number of pixels of unchanged
sessment. With the ratio of 7:1:2, these image pairs are objects that are falsely detected as changed objects,
split into the training set, validation set, and testing set. TN (True Negative) denotes the number of pixels of
Following the initial settings, we crop each image into 16 unchanged objects that are correctly regarded as non-
non-overlapped small patches with the size of 256Γ256 change, and FN (False Negative) denotes the number of
pixels. Thus, there are a total of 7120 image pairs for changed pixels that are not detected as changed objects.
training, 1024 for validation, and 2024 for testing.
4.3. Experimental Results and Analysis
4.2. Comparative method and Evaluation
In our experiments, the proposed FrNet is implemented
Metrics with the Pytorch platform using a single NVIDIA A100
To verify the effectiveness of the proposed FrNet method, GPU (with 40-GB RAM). During the training stage, the
four representative deep-learning-based change detec- Adam optimizer with a weight decay of 1π β 5 was em-
tion networks are taken into consideration. The FC-EF ployed. The batch size is set to 32, and the learning rate
[7] is an early fusion method based on U-Net that con- is initially set to 1π β 4 and will linearly reduce to 0 over
catenates the bitemporal image pairs as the input. And its 50,000 iterations. The π½ of the Hopfield layer is set to
extended versions, the FC-Siam-diff and FC-Siam-conc 1/ ππ .
β
[7], use Siamese networks with shared weights to ex- The quantitative results for the precision, recall, F1
tract multi-level features and use feature difference and score, and OA of all models are summarized in Table 1.
concatenation, respectively, to fuse bitemporal informa- It can be found that FC-EF obtains the lowest F1 score
tion. The bitemporal image transformer (BIT) network (75.25%) and OA (96.78%) among all the models. The FC-
[12] designs a context-information-based enhancer to Siam-conc and FC-Siam-diff perform slightly better than
extract related concepts in the token-based space-time, FC-EF, which indicates the Siamese network and feature
and projects the context-rich tokens back to original fea- difference/concatenation have benefits for the preserva-
tures for prediction. To validate the effectiveness of the tion of useful information. The F1 score and OA of the
(a) (b) (c) (d) (e) (f) (g) (h) (i)
Figure 3: Visualization results of different methods using the LEVIR-CD data set. (a) T1 Image; (b) T2 Image; (c) Ground-truth;
(d) FC-EF; (e) FC-Siam-conc; (f) FC-Siam-diff; (g) BIT; (h) Base Model; (i) FrNet. Yellow, black, red, and green represent TP, TN,
FP, and FN, respectively.
BIT model are 83.22% and 98.06%, respectively, better 5. Conclusion
than other FC-based models. This demonstrates that the
tokens in spase-time can effectively capture the tempo- Inspired by the successful application of continuous mod-
ral changes and enhance the context information. The ern Hopfield for pattern retrieval, we propose a deep
proposed FrNet achieves the highest F1 and OA among feature retrieved network (FrNet) for bitemporal change
all the studied methods and has better performance than detection. Our Hopfield pooling block introduces a train-
our base model. The improvements prove that the Hop- able weight matrix that aims to retrieve the global change
field layer helps retrieve the deep features and the shared of interests for high-level features and capture the dis-
query matrix can learn important information as part of criminative representations of one period related to the
the inputs for the decoder. other. To valuate the effectiveness of the proposed model,
Fig. 3 illustrates change detection maps obtained by experiments are conducted on the LEVIR-CD data set.
different methods, where TPs, TNs, FPs, and FNs are Our empirical evidence confirms the superiority of the
represented in yellow, black, red, and green, respec- proposed FrNet in comparison with other state-of-the-
tively. We can observe that FrNet achieves the best results arts methods.
among all the models. Firstly, FrNet can better distin-
guish small-sized changed buildings that have relatively
regular shapes by reducing false alarms compared with
Acknowledgments
other methods (e.g., the 1st, 2nd, and 3rd rows of Fig. 3). The authors would like to thank the contributors of the
When the shapes of buildings are complex, our model LEVIR-CD data set for making it publicly available, and
can also preserve the boundary of the objects (e.g., the the authors of the FC-EF, FC-Siam-conc, FC-Siam-diff,
4th, 5th, and 6th rows of Fig. 3). and the BIT methods for releasing their codes.
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