Semantic segmentation of multi-source remote sensing image based on multi-path context module
Year: 2026
Type: article
Abstract: The rapid development of remote sensing sensors and their platforms has dramatically increased the ways to acquire multi-source remote sensing data. However, how to make full use of the complementary characteristics of multi-source data to make up for the similarity between classes has become a challenge for remote sensing intelligent interpretation. Combining the redundancy and complementarity characteristics of multi-source data, firstly, we the Multi-path Connection and Context Module Network (MCM-Net) in this paper. Second, we construct a multi-path cascade mechanism to effectively fuse multi-source data’s complementary features, thereby suppressing noise and improving boundary accuracy. Then, we adopt the context enhancement module to aggregate feature maps of different scales and extract global information to better characterize the category’s discriminative features. Finally, extensive experiments on the US3D dataset demonstrate the effectiveness of our method. Specifically, MCM-Net achieves a mean Intersection over Union (mIoU) of 65.35%, outperforming the baseline U-Net by 3.5% and surpassing other state-of-the-art semantic segmentation methods.
Source: Discover Applied Sciences
Authors: Di Zhang, Yuhang Yan
Institution: Northwest Normal University
Language: English
Subfield: Media Technology
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7128433797