Spatial and Channel Exchange based on EfficientNet for Detecting Changes of Remote Sensing Images

Renfang Wang, Zijian Yang, Hong Qiu*, Xiufeng Liu, Dun Wu

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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 languageEnglish
Title of host publicationProceedings of the 2023 26th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2023
Number of pages6
PublisherInstitute of Electrical and Electronics Engineers Inc.
Publication date2023
Pages1595-1600
ISBN (Electronic)9798350331684
DOIs
Publication statusPublished - 2023
Event26th International Conference on Computer Supported Cooperative Work in Design - Rio de Janeiro, Brazil
Duration: 24 May 202326 May 2023
Conference number: 26

Conference

Conference26th International Conference on Computer Supported Cooperative Work in Design
Number26
Country/TerritoryBrazil
CityRio de Janeiro
Period24/05/202326/05/2023
SponsorIEEE, Kunming University, Université de technologie de Compiègne, Yunnan University, Zhejiang University

Keywords

  • Change Detection (CD)
  • Convolutional Neural Network (CNN)
  • Remote Sensing (RS)

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