Change Detection Based on Existing Vector Polygons and Up-to-Date Images Using an Attention-Based Multi-Scale ConvTransformer Network
Abstract
:1. Introduction
- We introduce a framework for detecting changes in vector polygons utilizing single-temporal high-resolution RS images and deep learning. This framework enables end-to-end application, encompassing image preprocessing through change detection, requiring solely up-to-date images and corresponding land cover vector data from the previous time image. This method offers a comprehensive bottom-up solution.
- For sample construction, we propose boundary-preserved masking Simple Linear Iterative Clustering (SLIC) for generating superpixels. These are then combined with land cover vector data to create an adaptive sample cropping scheme. To address noise, we introduce an efficient Visual Transformer and class-constrained Density Peak-based (EViTCC-DP) method for noisy label removal, followed by the transformation of noisy samples into representative ones using k-means clustering, resulting in the automatic generation of a high-quality multi-scale sample set.
- To enhance fine-grained scene classification precision, we employ an improved attention-based multi-scale ConvTransformer network (AMCT-Net) for superpixel cropping unit classification. By integrating a CNN structure and Transformer, along with the attention mechanism module, we achieve a more discriminative feature representation, enhancing the model’s classification accuracy. Additionally, we introduce a change decisionmaker with various rules, which synergistically combines and post-processes sample predictions with land cover vector data to effectively extract changed vector polygons.
2. Methodology
2.1. Density Peak Clustering
- The density around the cluster center should be relatively high;
- The cluster center should be situated at a considerable distance from points with higher surrounding density.
2.2. Automated Generation of Initial Samples with Vector Boundary Constraints
2.2.1. Automatic Generation of Initial Samples
2.2.2. Source of Noise Samples
2.3. Initial Samples Denoising Based on DP Clustering Algorithm
- Employing boundary constraints of vector polygons and adaptive cropping of RS images to automatically generate initial samples and train ViT models.
- Utilizing the pre-trained ViT to extract features from scene samples and inputting them into the DP clustering algorithm according to class constraints to achieve the purpose of denoising.
2.4. Attention-Based Multi-Scale ConvTransformer Network, AMCT-Net
2.4.1. Overview of the Proposed AMCT-Net
2.4.2. Module Details
2.5. Vector Polygons Change Detection Analysis Based on Confidence Rules
3. Experiments and Results
3.1. Description of Data Sources and Research Scheme
3.2. Results
3.2.1. Change Detection and Post-Processing
3.2.2. Evaluation Metrics
3.2.3. Vector Polygons Change Detection Results
- Across both datasets, the baseline model (ViT) exhibits unsatisfactory performance across the five evaluation metrics, while the enhanced model incorporating attention mechanisms and a multi-scale convolution module demonstrates a notable improvement in accuracy. Notably, the AMCT-Net model outperforms other architectures in terms of Recall, specificity, and F1 score. Specifically, on the Nantong dataset, AMCT-Net achieves a Recall of 0.9134, specificity of 0.9839, and F1 score of 0.9201, representing a 0.25% increase in Recall compared to the sub-optimal HCTM model (Recall = 0.9109). On the Guantan dataset, AMCT-Net’s Recall reaches 0.9292, specificity stands at 0.9898, and F1 score is 0.9306, with a significant 1.92% increase in Recall compared to the sub-optimal HCTM model (Recall = 0.9100). This underscores the substantial advancement in classification accuracy achieved by AMCT-Net.
- It is noteworthy that the performance of the model differs between the two datasets. For instance, the proposed AMCT-Net model only marginally improves accuracy by 0.66% compared to the baseline model on the Nantong dataset. Conversely, the model exhibits a much more substantial improvement in accuracy on the Guantan dataset, with an increase of 1.86% compared to the baseline model. The variation in performance may be attributed to the urban development context of the Nantong dataset, where change types are inherently more complex compared to the Guantan dataset. However, as AMCT-Net integrates the local feature extraction capabilities of CNN structures with the global information processing characteristics of Transformer architecture, supplemented by the introduction of a multi-scale module, these enhancements prove particularly advantageous for processing the multi-scale sample set in this study, underscoring its adaptability to diverse dataset features.
4. Analysis and Discussion
4.1. Analysis of the DP Algorithm Parameters Selections
4.2. Influence of Sample Set Denoising
4.3. Introducing Representative Training Samples
5. Conclusions
- The boundary constraint segmentation method utilized in this study accurately segments the boundaries of ground objects, while the adaptive cropping strategy facilitates comprehensive sampling within vector polygons, minimizing confusion among ground objects in the generated samples. The proposed sample denoising method, EViTCC-DP, significantly enhances model accuracy, leading to a 2.80% and 2.56% improvement in OA on the Nantong and Guantan datasets, respectively.
- To enhance classification performance, we introduced multi-scale modules and attention mechanisms to construct a novel model, AMCT-Net. This network combines the advantages of CNNs and Transformers, enabling the extraction of more discriminative features. Experimental results on the two datasets demonstrate the effectiveness of the proposed method, with the accuracy of the AMCT-Net model reaching 91.34% and 93.51%, respectively, surpassing that of other advanced models.
- Visual interpretation results demonstrate the significance of RTS in enhancing detection accuracy. The introduction of RTS yields a 2.11% and 1.09% increase in change detection accuracy for the Nantong and Guantan datasets, respectively. Our approach enables the swift construction of a high-quality multi-scale scene sample set incorporating RTS, requiring minimal manual intervention. Furthermore, in conjunction with designed change decision rules featuring adjustable parameters and improved applicability, the change detection method outlined in this paper effectively identifies changed vector polygons, offering clear advantages over traditional manual vector polygons updating methods.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Number | Land Use Types | Nantong Dataset | Guantan Dataset |
---|---|---|---|
C1 | buildings | 5243 | 4849 |
C2 | cropland | 2647 | 1744 |
C3 | forest | 3201 | 3768 |
C4 | industrial | 5844 | 4160 |
C5 | paddy field | 4848 | 5115 |
C6 | road | 2738 | 1240 |
C7 | water | 5690 | 5139 |
Model | Metrics | Nantong Dataset | Guantan Dataset |
---|---|---|---|
Accuracy | 0.9068 | 0.9165 | |
Precision | 0.8938 | 0.8914 | |
ViT | Recall | 0.8903 | 0.9072 |
Specificity | 0.9816 | 0.9811 | |
F1 Score | 0.8921 | 0.8992 | |
MTC-Net | Accuracy | 0.9117 | 0.9164 |
Precision | 0.9103 | 0.8969 | |
Recall | 0.8904 | 0.9203 | |
Specificity | 0.9824 | 0.9811 | |
F1 Score | 0.8985 | 0.8996 | |
HCTM | Accuracy | 0.9135 | 0.9292 |
Precision | 0.8975 | 0.9230 | |
Recall | 0.9109 | 0.9100 | |
Specificity | 0.9817 | 0.9859 | |
F1 Score | 0.9182 | 0.9215 | |
AMCT-Net (ours) | Accuracy | 0.9134 | 0.9351 |
Precision | 0.9179 | 0.9228 | |
Recall | 0.9134 | 0.9292 | |
Specificity | 0.9839 | 0.9898 | |
F1 Score | 0.9201 | 0.9306 |
Training Set | Epoch | ||||||
---|---|---|---|---|---|---|---|
10 | 20 | 30 | 40 | 50 | 82 | ||
Initial | OA | 0.8153 | 0.8459 | 0.8644 | 0.8798 | 0.8984 | 0.9068 |
Denoised by TCCV | OA | 0.8342 | 0.8709 | 0.8804 | 0.8979 | 0.9079 | 0.9288 |
Denoised by EViTCC-DP | OA | 0.8420 | 0.8727 | 0.8875 | 0.9052 | 0.9129 | 0.9348 |
Training Set | Epoch | ||||||
---|---|---|---|---|---|---|---|
10 | 20 | 30 | 40 | 60 | 84 | ||
Initial | OA | 0.8063 | 0.8379 | 0.8595 | 0.8752 | 0.8906 | 0.9165 |
Denoised by TCCV | OA | 0.8389 | 0.8711 | 0.8888 | 0.9010 | 0.9093 | 0.9333 |
Denoised by EViTCC-DP | OA | 0.8441 | 0.8771 | 0.8953 | 0.9089 | 0.9197 | 0.9421 |
Training Set | % | Nantong Dataset | Guantan Dataset |
---|---|---|---|
Denoised (excluding RTS) | Precision | 88.22 | 89.97 |
Recall | 91.26 | 92.13 | |
Denoised (including RTS) | Precision | 90.33 | 91.06 |
Recall | 91.41 | 92.38 |
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Wang, S.; Zhu, Y.; Zheng, N.; Liu, W.; Zhang, H.; Zhao, X.; Liu, Y. Change Detection Based on Existing Vector Polygons and Up-to-Date Images Using an Attention-Based Multi-Scale ConvTransformer Network. Remote Sens. 2024, 16, 1736. https://doi.org/10.3390/rs16101736
Wang S, Zhu Y, Zheng N, Liu W, Zhang H, Zhao X, Liu Y. Change Detection Based on Existing Vector Polygons and Up-to-Date Images Using an Attention-Based Multi-Scale ConvTransformer Network. Remote Sensing. 2024; 16(10):1736. https://doi.org/10.3390/rs16101736
Chicago/Turabian StyleWang, Shengli, Yihu Zhu, Nanshan Zheng, Wei Liu, Hua Zhang, Xu Zhao, and Yongkun Liu. 2024. "Change Detection Based on Existing Vector Polygons and Up-to-Date Images Using an Attention-Based Multi-Scale ConvTransformer Network" Remote Sensing 16, no. 10: 1736. https://doi.org/10.3390/rs16101736