Abstract
Change detection is an important branch in remote sensing image processing. Deep learning has been widely used in this field. In particular, a wide variety of attention mechanisms have made great achievements. However, some models have become increasingly complex and large, often unfeasible for edge applications. This poses a major obstacle to industrial applications. In this paper, to solve the above challenges, we propose a Lightweight network structure to improve results while taking into account efficiency. Specifically, first, the shallow features are extracted by using the spatial exchange and change exchange of the down-sampling bi-temporal channel of the three-layer EfficientNet backbone network, and then the shallow features are used for low-dimensional skip-connection. After that, a hybrid dual-temporal data module is designed to mix the dual-temporal phase into a single image, then the high-dimensional low-pixel image is restored through the up-sampling. Finally the final change map is generated through the pixel-level classifier. Our method was evaluated on public datasets by evaluation indicators such as OA, IoU, F1, Recall, Precision.
Original language | English |
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Title of host publication | Proceedings of the 2023 26th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2023 |
Number of pages | 6 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Publication date | 2023 |
Pages | 1595-1600 |
ISBN (Electronic) | 9798350331684 |
DOIs | |
Publication status | Published - 2023 |
Event | 26th International Conference on Computer Supported Cooperative Work in Design - Rio de Janeiro, Brazil Duration: 24 May 2023 → 26 May 2023 Conference number: 26 |
Conference
Conference | 26th International Conference on Computer Supported Cooperative Work in Design |
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Number | 26 |
Country/Territory | Brazil |
City | Rio de Janeiro |
Period | 24/05/2023 → 26/05/2023 |
Sponsor | IEEE, Kunming University, Université de technologie de Compiègne, Yunnan University, Zhejiang University |
Keywords
- Change Detection (CD)
- Convolutional Neural Network (CNN)
- Remote Sensing (RS)