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App. Res. of Comp. Accepted Paper
Vol. 42, 2025 No. 11

Ringnet:semantic segmentation of romote sensing images based on ring convolution

Xu Rui1
Han Bin1
Chen Fei1
Zhang Zihan2
1. Jiangsu University of Science & Technology, School of Computer, Zhenjiang Jiangsu 212100, China
2. Nanjing Agriculture University, Nanjing, School of artificial intelligence&, Nanjing Jiangsu 210095, China
DOI: 10.19734/j.issn.1001-3695.2025.03.0099

Abstract

To address the limitations of existing encoder–decoder-based semantic segmentation algorithms for remote sensing images—particularly in terms of insufficient contextual understanding and poor small-object recognition—this paper proposes a novel semantic segmentation network called RingNet, which is built upon ring-shaped convolution. By introducing ring convolution layers to encode semantic features with radial or circular distributions, RingNet incorporates two key modules: a Ring Residual Module (RingRes) and a Ring-Shaped Pyramid Convolution Module (RingSPP) to capture multi-scale contextual information. The network adopts ResNet18 as its backbone and integrates the RingRes module in the shallow layers to expand the receptive field while preserving original texture information. In the deeper layers, RingSPP leverages ring convolutions of various radii combined with channel attention mechanisms to extract rich semantic and spatial features. Experiments conducted on the Potsdam and Vaihingen high-resolution remote sensing datasets demonstrate that RingNet outperforms mainstream segmentation models in terms of mean overall accuracy, F1-score, and mIoU, and achieves superior performance in preserving semantic details and object boundaries. The ring convolution method has been proven to have an improved effect in the semantic segmentation task of remote sensing images.

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.03.0099
Publish at: Application Research of Computers Accepted Paper, Vol. 42, 2025 No. 11

Publish History

[2025-07-04] Accepted Paper

Cite This Article

徐睿, 韩斌, 陈飞, 等. 基于环形卷积的遥感影像语义分割方法 [J]. 计算机应用研究, 2025, 42 (11). (2025-07-08). https://doi.org/10.19734/j.issn.1001-3695.2025.03.0099. (Xu Rui, Han Bin, Chen Fei, et al. Ringnet:semantic segmentation of romote sensing images based on ring convolution [J]. Application Research of Computers, 2025, 42 (11). (2025-07-08). https://doi.org/10.19734/j.issn.1001-3695.2025.03.0099. )

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.

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