Multi-scale Change-Aware Transformer for Remote Sensing Image Change Detection

Venue: acmmm_org_ACMMM_2024_Conference
Published: 2024-04-03 17:22:59

Abstract: Change detection identifies differences between images captured at different times. Real-world change detection faces challenges from the diverse and intricate nature of change areas, while current datasets and algorithms are often limited to simpler, uniform changes, reducing their effectiveness in practical application. Existing dual-branch methods process images independently, risking the loss of change information due to insufficient early interaction. In contrast, single-stream approaches, though improving early integration, lack efficacy in capturing complex changes. To address these issues, we introduce a novel single-stream architecture, the Multi-scale Change-Aware Transformer (MACT), which features the Dynamic Change-Aware Attention module and the Multi-scale Change-Enhanced Aggregator. The Dynamic Change-Aware Attention module, integrating local self-attention and cross-temporal attention, conducts dynamic iteration on images differences, thereby targeting feature extraction of change areas. The Multi-scale Change-Enhanced Aggregator enables the model to adapt to various scales and complex shapes through local change enhancement and multiscale aggregation strategies. To overcome the limitations of existing datasets regarding the scale diversity and morphological complexity of change areas, we construct the Mining Area Change Detection dataset. The dataset offers a diverse array of change areas that span multiple scales and exhibit complex shapes, providing a robust benchmark for change detection. Extensive experiments demonstrate that the our model outperforms existing methods, especially for irregular and multi-scale changes.

Main paper: 
 introduction: 
 Change Detection (CD) identifies changes in surface or phenomena over time by analyzing images captured at different time points. These changes range from local alterations, such as the construction or demolition of buildings [18, 24] , to broader regional transformations like deforestation [23] and urban expansion [8, 25] . Additionally, change areas present irregular boundaries and diverse shapes, influenced by factors such as natural terrain, human activities and varying image resolutions. Consequently, change detection algorithms and datasets are required to capture the complex and multi-scale changes for practical monitoring. Despite providing valuable data on urban growth and agricultural changes (see Tab. 1), datasets are limited by their focus on uniform, small-scale changes. This restricts the capacity of current algorithms to accurately detect complex, multi-scale changes, reducing their practical effectiveness.Current change detections [1, 2, 4, 5] often use dual-branch pipelines that extract and later integrate features from dual-temporal images, as shown in Fig. 1(a) . These methods extract features from image pairs individually but fail to integrate early cross-temporal changes effectively, impacting accurate detection and increasing model complexity. Alternatively, a few algorithms use a singlestream approach by merging image pairs for simultaneous processing, which enhances the complementarity of information between images. However, single-stream methods, though effective for regular changes, often struggle with capturing details and adapting to multi-scale changes, particularly in complex change scenarios.In response to the limitations faced by current change detection methods in capturing complex variations, we expect to facilitate interaction between image pairs at the early stage of image processing to refine feature extraction within change areas, as illustrated in Fig. 1(b) . Additionally, we are committed to enhancing the model's ability to recognize multi-scale and complex change areas, thereby improving its effectiveness in practical applications.Therefore, we introduce an single-stream architecture termed the Multi-scale Change-Aware Transformer (MACT). The framework adopts an encoder-decoder structure, with the Change-Aware Encoder composed of a stack of Dynamic Change-Aware Attention (DCAA) modules. The Dynamic Change-Aware Attention (DCAA) mechanism is designed to facilitate dynamic image comparison across disparate time points by harnessing the power of local selfattention and cross-temporal attention. The local self-attention mechanism extracts features from individual images and the crosstemporal attention compares the changes between two images. Furthermore, the Dynamic Change-Aware Attention adjust the size of local windows at different stages, facilitating a comprehensive and precise capture of variations across various scales. This earlystage deep feature interaction enables the encoder to optimize the recognition of features within change regions, thereby enhancing overall change detection performance.To enhance the Multi-scale Change-Aware Transformer's capacity to detect changes across various scales, we integrate the Multis-cale Change-Enhanced Aggregator (MCEA). MCEA consists of two complementary sub-modules: the Change-Enhanced Module (CEM) and the Multi-scale Change Aggregator (MCA). The Change-Enhanced Module employs a local attention to emphasize emphasize regions within the image that exhibit significant changes. Through Depthwise Convolution, it refines the local details of features. Subsequently, the Multi-scale Change Aggregator integrates the multi-scale features output by the Change-Enhanced Module across different levels. Through a hierarchical processing strategy, Multi-scale Change Aggregator preserves local details and promotes the integration of high-level semantic information, enhancing the model's overall understanding of complex change.To overcome the limitation of existing change detection datasets, which focus on changes with regular shapes and limited scales, we introduce the Mining Area Change Detection (MACD) dataset. MACD covers a broader range of multi-scale change areas, including irregular shapes and intricate boundary conditions, aligning closely with the complexities encountered in real-world monitoring scenarios. The Mining Area Change Detection dataset not only enriches the benchmark data for change detection but also provides a more challenging test platform, particularly in improving the detection accuracy of irregular changes.Extensive experiments demonstrate that the Multi-scale Change-Aware Transformer has achieved state-of-the-art performance, particularly in handling complex and multi-scale change scenarios. Our contributions can be summarized as follows:• We introduce Multi-scale Change-Aware Transformer, a novel framework leveraging Dynamic Change-Aware Attention for efficient and parallel feature extraction, with a focus on capturing features within change regions. • We develop Multi-scale Change-Enhanced Aggregator, a component augmenting the model's ability to perceive changes at multiple scales by aggregating and refining features. • We construct the Mining Area Change Detection dataset, which introduces complex and varied change area morphologies, providing a rich and challenging benchmark for the field of change detection. 
related work: 
 Change Detection. Currently, the majority of change detection algorithms [10, 20, 27, 29] adopt a dual-branch architecture. This architecture involves independent feature extraction from dualtemporal images, fusion of these features, and prediction of change maps. Early methods [6, 16] utilized fully convolutional neural networks for feature extraction, followed by simple operations like addition or subtraction for feature fusion. In modern change detection frameworks, mechanisms such as spatial and channel attention are incorporated to enhance performance. For example, DASNet [5] combines these attention mechanisms, while DTCDSCN [17] leverages dual attention modules to exploit feature interdependencies. However, dual-branch approaches have limitations in fully integrating complementary information between dual-temporal images and often involve complex models with numerous parameters. Although single-stream structures simplify model design and facilitate interaction between image pairs, they still fall short in capturing local details and adapting to multi-scale changes, especially in complex change scenarios. This study builds upon the single-stream structure by enabling interaction between image pairs, thereby enhancing the model's flexibility and adaptability in complex scenes.Transformer-Based Methods for Change Detection. Transformers, renowned for their robustness and high performance, are increasingly utilized in various change detection methods. For instance, MSTDSNet-CD [22] and SwinSUNet [26] employ dualstream networks for feature extraction and multi-scale aggregation, albeit with high computational demands. Conversely, BIT [3] proposes a lightweight model featuring a single-stream transformer and two decoders for efficient spatiotemporal context modeling.ChangeFormer [1] combines hierarchical transformer encoders and multi-layer perceptron decoders in a Siamese network structure. Despite their commendable performance, these methods predominantly rely on dual-stream architectures. In this paper, we introduce a novel perspective by embracing a single-stream transformer.Datasets for Change Detection. The mainstream change detection datasets are summarized in Tab. 1. These datasets can be roughly categorized into three types: the first category includes datasets focusing on building changes, such as LEVIR [4] , WHU [12] , and ABCD [11] , as well as those involving land cover changes like ZY3 [28] and OSCD [7] . The second category comprises datasets containing multiple types of change targets, such as CCD [14] and SYSU [21] . However, these datasets often only contain small-scale and regularly shaped change areas, posing significant obstacles when dealing with complex change regions in practical applications.To overcome these limitations, we propose a new dataset-Mining Area Change Detection. This dataset encompasses diverse and morphologically complex change regions, presenting new challenges and higher difficulty levels for change detection tasks. 
method: 
 Given a pair of input dual-temporal images 𝑰 1 and 𝑰 2 , each with a shape of H × W × 3,our goal is to learn a mapping function 𝚽(•).The 𝚽(•) takes 𝑰 1 and 𝑰 2 as inputs and predicts the change map 𝑷 ∈ R H×W . Mathematically, this process can be represented as: To realize the mapping function 𝚽(•), we propose the Multi-scale Change-Aware Transformer (MACT). Firstly, the Change-Aware Encoder of the MACT employs the Dynamic Change-Aware Attention to extract key features from the dual-temporal images and dynamically compare changes between image pairs. Subsequently, the extracted features are passed to the Change-Enhanced Multiscale Aggregator, which enhances sensitivity to change areas through aggregation strategies. Finally, a simple decoder transforms features into the final change map 𝑷 , providing a detailed representation of the changes between input image pair.EQUATIONThe Change-Aware Encoder is designed for feature extraction and relationship modeling of image pairs, ensuring the preservation of information about the change areas from early stages of processing.The encoder is stacked with Dynamic Change-Aware Attention (DCAA) blocks and combines patch embedding or patch merging layers, as shown in Fig. 2(a) . It consists of four stages, with each stage containing N 𝑖 attention blocks, where 𝑖 ranges from {1, 2, 3, 4}. The images 𝑰 1 and 𝑰 2 first undergo a patch embedding layer to reduce spatial resolution and expand channel depth. Subsequently, they are processed by Dynamic Change-Aware Attention blocks across the four stages, yielding a series of multi-scale features 𝒀 𝑖 .The Dynamic Change-Aware Attention block, depicted in Fig. 2 (b), serves as the cornerstone of the Change-Aware Encoder, which utilizes local self-attention to extract features and employs crosstemporal attention to contrast differences between images, thereby facilitating change perception. The Dynamic Change-Aware Attention initiates by extracting attention elements from input images through local expansion operations. It then employs a multi-head attention to process these elements. Furthermore, Dynamic Change-Aware Attention strategically applies windows of varying sizes across different stages, facilitating a nuanced capture of changes at multiple scales. Gathering Attention Elements. Formally, given dual-temporal image features 𝑿 𝑖 and 𝒁 𝑖 with dimensions H 𝑖 × W 𝑖 × C 𝑖 . H 𝑖 and W 𝑖 represent the feature height and width at the 𝑖-th stage, calculated as H 𝑖 = H 2 𝑖+1 and W 𝑖 = W 2 𝑖+1 . C 𝑖 is number of channels for features. Next, the Depth-wise Separable Convolution (DWConv) is used to project features, generating queries (𝑸), keys (𝑲 ) and values (𝑽 ):EQUATIONEQUATIONhere, 𝑾Q 𝑥 𝑖 , 𝑾 K 𝑥 𝑖 , 𝑾 V 𝑥 𝑖 , 𝑾 Q 𝑧 𝑖 , 𝑾 K 𝑧 𝑖, and 𝑾 V 𝑧 𝑖 are the weight parameters of the convolution layer. To simplify the expression, we omit the subscript 𝑖 for the related representations of 𝑸, 𝑲 and 𝑽 . Local Expansion Operation. To integrate local information and enhance sensitivity to detail changes, we perform local expansion operations on the keys 𝑲 x and 𝑲 z . This operation effectively expands the local receptive field by considering neighborhood information around each point. Specifically, we use the Unfold function to extract L 𝑖 × L 𝑖 regions around the keys on the feature maps, . This process can be mathematically described as:EQUATIONEQUATIONThe local window size L 𝑖 is adjusted according to the resolution of the feature maps at the current stage. In the early stages, a smaller window is employed to capture finer local changes, while in later stages, the window size is increased to cover a broader context, thereby detecting larger-scale changes. This dynamic window adjustment strategy enables the model to effectively capture changes at multiple scales, while optimizing the balance between computational efficiency and detection accuracy. Dynamic Difference Values. In the cross-temporal attention, we devise a difference iteration process tailored to bolster the model's capability to recognize change regions. This process computes the difference between 𝑽 x and 𝑽 z . The feature difference is computed through an absolute value operation, as depicted by the equation:EQUATIONSubsequently, the 𝑽 c is utilized as the value component in the crosstemporal attention mechanism, enabling the model to focus on change regions. Thus far, we obtain the representations for queries and keys as 𝑸 x , 𝑲 ′x , 𝑲 ′ z , and for values as 𝑽 x , 𝑽 z , 𝑽 c , with the following dimensions:𝑸 x , 𝑸 z ∈ R H 𝑖 ×W 𝑖 ×C 𝑖 ×1 , 𝑲 ′ x , 𝑲 ′ z ∈ R H 𝑖 ×W 𝑖 ×C 𝑖 ×L 𝑖 and 𝑽 x , 𝑽 z , 𝑽 c ∈ R H 𝑖 ×W 𝑖 ×C 𝑖 ×1 .Calculating Change-Aware Attention. As illustrated in Fig. 2 (b), we partition the attention heads into three groups, with two groups dedicated to extracting features from the images at two time points using local self-attention mechanisms, while the third group employs cross-temporal attention mechanisms specifically focused on the features of change areas. Specifically, we segment the channels of the query features 𝑸 x into 𝑸 xs and 𝑸 xc , and perform the same operation for the key features 𝑲 x to obtain 𝑲 zs and 𝑲 zc . Within each attention head group, the multi-head attention mechanism is utilized to compute attention features as follows:EQUATIONEQUATIONEQUATIONwhere MHA(•) denotes the multi-head attention function. Subsequently, we concatenate the outputs of all head groups along the feature dimension to obtain Ỹ , which is then processed through a multi-layer perceptron (MLP) to generate the final features 𝒀 :EQUATIONEQUATIONwhere EQUATIONEQUATIONEQUATIONwhere 𝒇 m is implemented by a fully connected layer. Next, we compute the Hadamard product of 𝑸 m and 𝑲 m , followed by aggregation of information through a DWConv layer to generate context-aware weights 𝑨 m :EQUATIONHere, 𝛿 (•) is the activation function tanh introducing nonlinearity to capture more complex feature relationships. Then, these weights are combined with 𝑽 m and passed through a softmax function 𝜎 (•) to generate the final enhanced features 𝑴 ′ , which are then fused with the input feature 𝒁 ′ :EQUATION)Through this combination, the Change-Enhanced Module effectively emphasizes local features related to changes, reducing interference from non-change areas on the decoder, thereby improving the overall performance of change detection. Multi-scale Change Aggregator. To fully exploit the potential of multi-scale features and capture complementary information across different levels, we propose a Multi-scale Change Aggregator. This aggregator operates in a bottom-up manner based on the enhanced features 𝑴 ′ generated by the Change-Enhanced Module. Specifically, the fused features 𝑴 ′ 𝑖 from lower levels are not only forwarded to the corresponding decoder levels but also propagated upwards to fuse with shallower features 𝑴 𝑖 -1 . This process is mathematically described by the function:EQUATIONwhere 𝑖 denotes the stage number, and 𝒇 MCA (•) is a function obtained through upsampling operations and a 3 × 3 convolutional kernel with learnable parameters. The upsampling operation adjusts the spatial resolution of features to match those of shallower features, while the convolutional kernel captures and integrates feature information from different levels. Through Multi-scale Change Aggregator, the model effectively integrates features from different levels, enhancing its capability to recognize changes across various scales.We employ the loss function set L to guide the model training. The overall loss is defined as follows:EQUATIONHere, L p is cross entropy loss that evaluates the accuracy of the final change map. In our model, indexed by 𝑗 over 𝑁 = 4 stages, the loss function integrates Weighted Binary Cross-Entropy (WBCE) for class balance, Structural Similarity Index (SSIM) for structural coherence, and Structural Intersection over Union (SIoU) for boundary precision, optimized by stage-specific weights 𝛼 𝑗 . 
mining area change detection dataset: 
 We introduce the Mining Area Change Detection (MACD) dataset, which is the first compilation specifically tailored for change detection in mining areas. Distinguished from urban construction and land cover monitoring datasets in Tab. 1, the MACD dataset encompasses a broad range of scale variations and intricate change Buildings ✗ ✗ ABCD [11] Buildings ✗ ✗ ZY3 [28] Land Cover✗ ✓ CCD [14] Complex Scenarios ✗ ✗ SYSU [21] Complex Scenarios ✓ ✗ MACD Mining Area ✓ ✓patterns, which is designed to enhance the performance of change detection algorithms in addressing a diversity of real-world issues.Data Collection. Our dataset is collected from various open-pit mining areas in the Chongqing region of China. These images are sourced from four Chinese satellites: Gaofen-1, Gaofen-2, Gaofen-6, and Ziyuan-3, with resolutions ranging from 2m to 0.8m. The imagery is acquired over a period from 2018 to August 2023, encompassing a variety of weather conditions, random variations in solar elevation angles, seasonal changes, differences in illumination, and sensor variations. These broad temporal scope and diverse conditions ensure the diversity and complexity of the dataset. Data Annotation. The dataset annotation is completed by experts with extensive experience in remote sensing image interpretation and profound understanding of mining configurations. The annotation process involved coarse localization, image registration, cropping, fine-grained annotation and generation of change maps. Detailed descriptions can be found in Supplementary Materials.Our dataset comprises a total of 2133 pairs of temporal images, partitioned into a training set with 1801 image pairs and a test set with 332 image pairs, all with a resolution of 128 × 128 pixels. We conduct a detailed statistical analysis to highlight the dataset's unique characteristics compared to other datasets. Scale Variation. Fig. 4 (a) illustrates the distribution of change area pixel ratios in both the training and testing sets of our dataset. The statistical graph reveals that the dataset significantly showcases a broad range of scale variations, covering change area pixel ratios from 10% to over 80%. Changes are categorized into (less than 5%), medium (5% to 15%), and large (greater than 15%) areas relative to the image size, as illustrated in Fig. 4 (b). The LEVIR [4] and WHU [12] datasets predominantly contain small change areas, while large changes are relatively rare. Our dataset, on the other hand, exhibits a balanced distribution across small, medium, and large change areas, a design intended to test the performance of change detection algorithms on different scales. Irregular Morphology. We use the ratio of the perimeter to area of change regions as a complexity metric, where a higher value indicates greater irregularity. As shown in Fig. 4 (c), the distribution of change region complexity across various datasets reveals that our mining area dataset contains regions with higher morphological complexity compared to urban structures or land cover datasets. Similarly, Fig. 4 (d) confirms the morphological complexity of mining area changes, characterized by irregular features such as fractures, overlaps, and voids. Therefore, the dataset requires algorithms to accurately identify complex boundaries and morphologies. 
experiments 5.1 implementation details: 
 We conduct experiments on our MACD, LEVIR [4] and WHU [12] dataset. The model is trained with the Adam optimizer [13] and exponential learning rate decay. The initial learning rate is set to 0.004, and the training process consists of 100 epochs with a batch size of 64. The comparative methods, evaluation metrics and more implementation details are detailed in the Supplementary Materials.Quantitative Results. Upon reviewing the data in Tab. 2, while SUNet achieves the highest accuracy at 74.30%, it exhibits variance in other critical metrics, indicating a possible inclination towards false positives. Comparatively, SARAS, with its scale sensitivity, delivers a more consistent and superior performance across the board. Our proposed method not only outshines SARAS but also significantly improves upon it, particularly with a 1.75% increase in recall-a testament to the effectiveness of our early feature interaction approach for pinpointing changes. Moreover, Our Transformer demonstrates a 0.29% enhancement in F1 score and a 0.33% uptick in IoU, underscoring its robustness and precision in change detection tasks. These quantitative leaps highlight our method's advanced capability in handling the nuanced complexities of change detection. Efficiency Analysis. From Tab. 2, Multi-scale Change-Aware Transformer,with 10.64M parameters and 34.94G FLOPS, strikes an effective balance between performance and efficiency. In contrast, models like FC-EF, FC-Siam-conc, FC-Siam-diff, and DASNet, despite their low parameter counts, underperform in terms of F1 score and IoU metrics. MACT outperforms SARAS, which has 102.7M parameters and 66.64G FLOPS, by reducing the parameter count and computational cost by 90.1% and 47.9%, respectively, without compromising performance. Although USSFC-Net is lightweight, it shows a notable performance deficit when handling complex data. This analysis underscores Multi-scale Change-Aware Transformer's superior performance and efficiency, outpacing existing models in both parameter count and computational cost. Qualitative Results. From Fig. 5 , it's evident that change areas in our dataset exhibit complex and irregular shapes. Overall, our method produces more satisfactory visual results, effectively handling both large-scale complete changes and intricate structures. In Fig. 5 (a), compared to other methods, we mitigate the impact of non-mining areas in the top left corner, reducing false positive predictions. Our approach achieves this by utilizing Dynamic Change-Aware Attention during the encoding stage to extract relevant change area features, as depicted in Fig. 5(c ). Unlike most comparative methods struggling to capture complete large-scale changes due to insufficient global and local information, our method maintains internal compactness by integrating multiscale features at various stages, ensuring the integrity of detection areas. Qualitative Results. Sample instances in Fig. 6 illustrate our method's excellent performance in detecting complex, large-area and small-area changes. In dense urban areas (as shown in Fig. 6 (a) and (b)), our method accurately identifies changes and reduces noise, outperforming other methods especially in edge detection of buildings. This advantage stems from the multi-scale strategy that integrates features at various levels and preserves edge details. In Additionally, we perform a two-dimensional projection of test set data points using t-distributed stochastic neighbor embedding (t-SNE). As shown in Fig. 8 (a), the initial features display a mixed state of changed and unchanged pixels. The method using direct global attention (Fig. 8(b) ) fails to effectively differentiate between the two types of pixels. In contrast, our Dynamic Change-Aware Attention (Fig. 8(c )) causes samples of the same class to cluster more tightly together. Finally, Fig. 8(d ) indicates that the Multi-scale Change-Enhanced Aggregator successfully strengthens inter-class differences, eliminating the interference of irrelevant information.In summary, our method consistently demonstrates strong performance. The Dynamic Change-Aware Attention and Multi-scale Change-Enhanced Aggregator encourage the model to effectively leverage and hierarchically aggregate local change features.To thoroughly validate the advantages of Multi-scale Change-Aware Transformer, a series of ablation studies are conducted. For details on model parameter selection, refer to the Supplementary Materials. Effect of Single-stream Pipeline. We conduct experiments with a comparative analysis of frameworks to validate the superiority of the single-stream architecture. By streamlining the backbone and head structures, we juxtapose our approach against a dualstream counterpart. Specifically, we develop a pyramidal structure,  
conclusion: 
 We introduce an innovative framework known as the Multiscale Change-Aware Transformer, which seamlessly integrates feature extraction and relationship modeling through a Dynamic Change-Aware Attention module, enhancing the feature extraction and interaction process between input image pairs. Furthermore, the Change-Enhanced Multiscale Aggregator targets the mining of multiscale features from change regions, significantly boosting the model's detection capabilities across various scales of change. In addition, we develop the Mining Area Change Detection dataset that encompasses complex morphologies and large-scale changes, effectively addressing the limitations of existing datasets that predominantly focus on changes with regular shapes. Our experimental results substantiate that our proposed method achieves state-ofthe-art performance levels in change detection tasks, particularly excelling in the handling of complex and large-scale changes.  
 
 Appendix:  


Decision: The AC goes through the paper, rebuttal and review comments.  This paper got 4 acceptances. After discussion, all the reviewers acknowledge the contribution of the novel change detection dataset. Nevertheless, the technical contribution of the proposed Multi-scale Change-Aware Transformer (MACT) can be further improved. In conclusion, the AC recommends accepting this paper.

Reviews: review: paper_summary: paper_summary: This manuscript  proposed a single-stream architecture,  Multi-scale Change-Aware Transformer (MACT) . It contains two main modules, i.e., Dynamic Change-Aware Attention module and Multi-scale Change-Enhanced Aggregator. The Dynamic Change-Aware Attention module, integrating local self-attention and cross-temporal attention, conducts dynamic iteration on images differencesn to extract  features of change areas. The Multi-scale Change-Enhanced Aggregator enables the model to adapt to various scales and complex shapes through local change enhancement and multiscale aggregation strategies.
It also constructs Mining Area Change Detection dataset, which introduces complex and varied change area morphologies.
strength_weakness: strength_weakness: The Mining Area Change Detection dataset is meaningful for change detection research. It offers a diverse array of change areas that span multiple scales and exhibit complex shapes. 
The proposed Multi-scale Change-Aware Transformer (MACT) performs well on MACD, LEVIR and WHU dataset.
limitations: limitations: 1. The writing needs to be  improved. There are many mistakes, for example, the sentence  "Changes are categorized into (less than 5%), medium (5% to 15%), and large (greater than 15%) areas relative to the image size, as illustrated in Fig. 4(b). " misses  the word "small". 
2. The loss contains three parts. The ablation experiments should be conducted on it to verify the effect of  these three parts.
3. In figure 1,  the structure of (c)(i)  is not  consistent with  the structure MCEA. A line from M1 to CEM is missed.
paper_summary: paper_summary: In this work, the authors collect a mining area change detection dataset that contains multiple scales and complex changing shapes. Besides, to improve the model's ability to interactively extract change information from remote sensing images, the authors propose the Multi-scale Change-Aware Transformer (MACT), which features the Dynamic Change-Aware Attention module and the Multi-scale Change-Enhanced Aggregator, reaching a good performance with less computational resources.
strength_weakness: strength_weakness: 1. The authors propose a novel change detection dataset containing complex changing areas, which can pose more challenges to current models.
2. This work introduces a single-branch encoder and proves that it outperforms its dual-branch counterpart. If this can be universally useful to all other change detection methods, people can reduce computation complexity and reach better performance at the same time.
3. The proposed method reaches good performances on the Mining Area, LIVER,  and WHU datasets.
limitations: limitations: 1. The proposed datasets contain 2133 pairs of images with $128 \times 128$ resolution. This is smaller than other existing datasets such as SYSU, WHU, etc. It is possible that limited training data could lead to over-optimization problems more easily.
2. The authors use the ratio of the perimeter to the area of change regions as a complexity metric, however, is this the best metric to evaluate complexity? For example, if the region is very small but still complex, the metric can hardly reflect the complexity.

[TYPOs]
1. Line 135: multi-scale
paper_summary: paper_summary: The paper discusses the challenges in remote sensing image change detection (CD) and introduces a new multi-scale change-aware transformer architecture called MACT. The proposed method presents a single-stream framework that contains a dynamic change-aware attention module and a multi-scale change-enhanced aggregator. MACT outperforms existing CD methods on the proposed dataset, MACD, and two popular benchmark datasets, LEVIR-CD and WHU-CD.
strength_weakness: strength_weakness: 1. The motivation for this work is clearly expressed.
2. The figures of the proposed method are well-drawn and easy to understand.
limitations: limitations: 1. The main issue of this work is that the details of the proposed multi-scale change-aware transformer are not shown in total, while the necessary source codes are not provided for review. Is this Transformer architecture related to the existing popular Transformer methods, e.g., Swin[1] and PVT[2]? Does it need to be pre-trained on ImageNet? And how are the hyperparameters set in the network, e.g., patch size, N_i {1,2,3,4}?

2. Eq. (18) shows that there are also four stages of predicted change maps for the proposed method. This is not made explicit in Figure 2 and Section 3.3.

3. In Figure 2, ‘DWConv’ indicates the depth-wise separable convolution, not only the depth-wise convolution.

4. In Eq. (10), the meaning of ‘CAT’ is not given. Is it concatenation? Also, Eq. (15) should make it clear that ‘\odot’ is the Hadamard product.

5. Transformer-based change detection methods should include LSAT [3], FHD [4], TransUNetCD [5], etc., in addition to SwinSUNET and ChangeFormer, which are introduced in the manuscript.

6. In the supplementary material, Table 1 should also give FLOPs and Params.

[1] Swin transformer: Hierarchical vision transformer using shifted windows, ICCV, 2021.
[2] Pyramid vision transformer: A versatile backbone for dense prediction without convolutions, ICCV, 2021.
[3] Lightweight Structure-Aware Transformer Network for Remote Sensing Image Change Detection, IEEE GRSL, 2023.
[4] Feature Hierarchical Differentiation for Remote Sensing Image Change Detection, IEEE GRSL, 2022.
[5] TransUNetCD: A Hybrid Transformer Network for Change Detection in Optical Remote-Sensing Images, IEEE TGRS, 2022.
paper_summary: paper_summary: This paper aims at solving change detection problems. Compared with previous methods, the authors introduce a single-stream architecture, the Multi-scale Change-Aware Transformer (MACT). It contains Dynamic Change-Aware Attention module and 	Multi-scale Change-Enhanced Aggregator to model the feature co-relation and mutli-scale detailed information. 
Extensive experiments on various change detection benchmarks show the effectiveness of proposed method.
strength_weakness: strength_weakness: 1, The proposed joint feature extraction and integration looks good and interesting to me. 

2, The overall writing is good and easy to follow. 

3, The proposed method MACT achieves stronger results on various benchmarks. 

4, The proposed Dynamic Change-Aware Attention is interesting.
limitations: limitations: 1, The novelties of Change-Enhanced Module (CEM) and Multi-scale Change Aggregator (MCA) are limited since both modules involve common operations including depth-wise convolution. 
Moreover, multi-scale feature fusion is nothing but common operation in many fields, including semantic segmentation and object detection. The authors only apply the fusion operation for change detection.

2, Missing one important comparison studies to indicates the effectiveness of joint feature extraction. 

3, Scope: I double this submission may not be suitable for ACM-MM. Maybe TGRS is better. 

I am not expert on this topic. I list the both strength and weakness and give the boardline rating for reference.