MAPMaN: Multi-Stage U-Shaped Adaptive Pattern Matching Network for Semantic Segmentation of Remote Sensing Images
| dc.contributor.author | Hong, Tingfeng | en_US |
| dc.contributor.author | Ma, Xiaowen | en_US |
| dc.contributor.author | Wang, Xinyu | en_US |
| dc.contributor.author | Che, Rui | en_US |
| dc.contributor.author | Hu, Chenlu | en_US |
| dc.contributor.author | Feng, Tian | en_US |
| dc.contributor.author | Zhang, Wei | en_US |
| dc.contributor.editor | Chaine, Raphaëlle | en_US |
| dc.contributor.editor | Deng, Zhigang | en_US |
| dc.contributor.editor | Kim, Min H. | en_US |
| dc.date.accessioned | 2023-10-09T07:37:36Z | |
| dc.date.available | 2023-10-09T07:37:36Z | |
| dc.date.issued | 2023 | |
| dc.description.abstract | Remote sensing images (RSIs) often possess obvious background noises, exhibit a multi-scale phenomenon, and are characterized by complex scenes with ground objects in diversely spatial distribution pattern, bringing challenges to the corresponding semantic segmentation. CNN-based methods can hardly address the diverse spatial distributions of ground objects, especially their compositional relationships, while Vision Transformers (ViTs) introduce background noises and have a quadratic time complexity due to dense global matrix multiplications. In this paper, we introduce Adaptive Pattern Matching (APM), a lightweight method for long-range adaptive weight aggregation. Our APM obtains a set of pixels belonging to the same spatial distribution pattern of each pixel, and calculates the adaptive weights according to their compositional relationships. In addition, we design a tiny U-shaped network using the APM as a module to address the large variance of scales of ground objects in RSIs. This network is embedded after each stage in a backbone network to establish a Multi-stage U-shaped Adaptive Pattern Matching Network (MAPMaN), for nested multi-scale modeling of ground objects towards semantic segmentation of RSIs. Experiments on three datasets demonstrate that our MAPMaN can outperform the state-of-the-art methods in common metrics. The code can be available at https://github.com/INiid/MAPMaN. | en_US |
| dc.description.number | 7 | |
| dc.description.sectionheaders | Learning and Image Processing | |
| dc.description.seriesinformation | Computer Graphics Forum | |
| dc.description.volume | 42 | |
| dc.identifier.doi | 10.1111/cgf.14978 | |
| dc.identifier.issn | 1467-8659 | |
| dc.identifier.pages | 11 pages | |
| dc.identifier.uri | https://doi.org/10.1111/cgf.14978 | |
| dc.identifier.uri | https://diglib.eg.org:443/handle/10.1111/cgf14978 | |
| dc.publisher | The Eurographics Association and John Wiley & Sons Ltd. | en_US |
| dc.subject | CCS Concepts: Computing methodologies -> Neural networks; Image segmentation | |
| dc.subject | Computing methodologies | |
| dc.subject | Neural networks | |
| dc.subject | Image segmentation | |
| dc.title | MAPMaN: Multi-Stage U-Shaped Adaptive Pattern Matching Network for Semantic Segmentation of Remote Sensing Images | en_US |