DGCFNet: Dual Global Context Fusion Network for remote sensing image semantic segmentation
Yuan Liao, Tongchi Zhou, Lu Li, Jinming Li, Jiuhao Shen, Askar Hamdulla
TL;DR
This paper introduces DGCFNet, a new network for segmenting remote sensing images by combining CNNs and Transformers to better capture local and global features.
Contribution
The novel DGCFNet integrates CNN and Transformer strengths with dual-branch global extraction and cross-level interaction modules for improved segmentation.
Findings
DGCFNet achieves mIoU scores of 82.20% on Vaihingen, 83.84% on Potsdam, and 68.87% on BLU datasets.
The dual-branch global extraction module enhances global context modeling while preserving local details.
Cross-level information interaction improves feature correlation across different levels for better segmentation.
Abstract
The semantic segmentation task of remote sensing images often faces various challenges such as complex backgrounds, high inter-class similarity, and significant differences in intra-class visual attributes. Therefore, segmentation models need to capture both rich local information and long-distance contextual information to overcome these challenges. Although convolutional neural networks (CNNs) have strong capabilities in extracting local information, they are limited in establishing long-range dependencies due to the inherent limitations of convolution. While Transformer can extract long-range contextual information through multi-head self attention mechanism, which has significant advantages in capturing global feature dependencies. To achieve high-precision semantic segmentation of remote sensing images, this article proposes a novel remote sensing image semantic segmentation…
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Taxonomy
TopicsAdvanced Image Fusion Techniques · Advanced Neural Network Applications · Remote-Sensing Image Classification