Cross-Domain Feature Fusion Network: A Lightweight Road Extraction Model Based on Multi-Scale Spatial-Frequency Feature Fusion
Road extraction is a key task in the field of remote sensing image processing. Existing road extraction methods primarily leverage spatial domain features of remote sensing images, often neglecting the valuable information contained in the frequency domain. Spatial domain features capture semantic i...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-02-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/4/1968 |
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| Summary: | Road extraction is a key task in the field of remote sensing image processing. Existing road extraction methods primarily leverage spatial domain features of remote sensing images, often neglecting the valuable information contained in the frequency domain. Spatial domain features capture semantic information and accurate spatial details for different categories within the image, while frequency domain features are more sensitive to areas with significant gray-scale variations, such as road edges and shadows caused by tree occlusions. To fully extract and effectively fuse spatial and frequency domain features, we propose a Cross-Domain Feature Fusion Network (CDFFNet). The framework consists of three main components: the Atrous Bottleneck Pyramid Module (ABPM), the Frequency Band Feature Separator (FBFS), and the Domain Fusion Module(DFM). First, the FBFS is used to decompose image features into low-frequency and high-frequency components. These components are then integrated with shallow spatial features and deep features extracted through the ABPM. Finally, the DFM is employed to perform spatial–frequency feature selection, ensuring consistency and complementarity between the spatial and frequency domain features. The experimental results on the CHN6_CUG and Massachusetts datasets confirm the effectiveness of CDFFNet. |
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| ISSN: | 2076-3417 |