In accordance with regulations and requirements, the editorial department's website domain has been changed to arocmag.cn. The original domain (arocmag.com) has been redirecting to new domain since Jan. 1st, 2025.
See More
App. Res. of Comp. Accepted Paper
Vol. 42, 2025 No. 4

Cnn and visual state space based semantic segmentation of remote sensing images

Zhang Shijiea,b
Zhang Bina,b
Zhao Wenhaoa,b
a. Hubei Province Key Laboratory of Intelligent Robot, b. School of Computer Science & Engineering, Wuhan Institute of technology, Wuhan 430205, China
DOI: 10.19734/j.issn.1001-3695.2024.07.0269

Abstract

With the development of deep learning, CNNs and transformers drive the widespread application of semantic segmentation in the field of remote sensing. However, these methods still face limitations: the former fails to model long-range dependencies effectively, while computational complexity constrains the latter. Recently, Mamba demonstrates that the visual state space (VSS) model efficiently computes long-range dependencies through linear operations. The design introduced a semantic segmentation network for remote sensing images, combining CNN and visual state space to overcome existing limitations. The architecture consisted of a CNN branch and a VSS branch, which extracted multi-scale features, explored local correlations, and captured long-range dependencies. It applied the VSS instead of the transformer to the decoder. A co-modulation module learned spatial weight modulation features, fused semantic information from both branches, and enhanced dependencies between them. An additional auxiliary header optimized the network, focusing the model on critical regions during training through the auxiliary loss function. The experiments validated the method on the LoveDA and Vaihingen datasets, achieving mF1 scores of 69.61% and 90.53% and mIoU scores of 53.95% and 83.13%, respectively. The experimental results show that the proposed model outperforms other segmentation models on these two public datasets.

Foundation Support

湖北省自然科学基金资助项目(2022CFCO31)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.07.0269
Publish at: Application Research of Computers Accepted Paper, Vol. 42, 2025 No. 4

Publish History

[2024-10-14] Accepted Paper

Cite This Article

张仕洁, 张斌, 赵文豪. 基于CNN和视觉状态空间的遥感影像语义分割 [J]. 计算机应用研究, 2025, 42 (4). (2024-12-31). https://doi.org/10.19734/j.issn.1001-3695.2024.07.0269. (Zhang Shijie, Zhang Bin, Zhao Wenhao. Cnn and visual state space based semantic segmentation of remote sensing images [J]. Application Research of Computers, 2025, 42 (4). (2024-12-31). https://doi.org/10.19734/j.issn.1001-3695.2024.07.0269. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

Indexed & Evaluation

  • The Second National Periodical Award 100 Key Journals
  • Double Effect Journal of China Journal Formation
  • the Core Journal of China (Peking University 2023 Edition)
  • the Core Journal for Science
  • Chinese Science Citation Database (CSCD) Source Journals
  • RCCSE Chinese Core Academic Journals
  • Journal of China Computer Federation
  • 2020-2022 The World Journal Clout Index (WJCI) Report of Scientific and Technological Periodicals
  • Full-text Source Journal of China Science and Technology Periodicals Database
  • Source Journal of China Academic Journals Comprehensive Evaluation Database
  • Source Journals of China Academic Journals (CD-ROM Version), China Journal Network
  • 2017-2019 China Outstanding Academic Journals with International Influence (Natural Science and Engineering Technology)
  • Source Journal of Top Academic Papers (F5000) Program of China's Excellent Science and Technology Journals
  • Source Journal of China Engineering Technology Electronic Information Network and Electronic Technology Literature Database
  • Source Journal of British Science Digest (INSPEC)
  • Japan Science and Technology Agency (JST) Source Journal
  • Russian Journal of Abstracts (AJ, VINITI) Source Journals
  • Full-text Journal of EBSCO, USA
  • Cambridge Scientific Abstracts (Natural Sciences) (CSA(NS)) core journals
  • Poland Copernicus Index (IC)
  • Ulrichsweb (USA)