SFANet: A Ground Object Spectral Feature Awareness Network for Multimodal Remote Sensing Image Semantic Segmentation.
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| Title: | SFANet: A Ground Object Spectral Feature Awareness Network for Multimodal Remote Sensing Image Semantic Segmentation. |
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| Authors: | Lan, Yizhou1 (AUTHOR), Zheng, Daoyuan2 (AUTHOR), Zheng, Yingjun1 (AUTHOR), Zhang, Feizhou1,2 (AUTHOR) zhangfz@pku.edu.cn, Xu, Zhuodong1 (AUTHOR), Shang, Ke1 (AUTHOR), Wan, Zeyu1 (AUTHOR) |
| Source: | Remote Sensing. May2025, Vol. 17 Issue 10, p1797. 23p. |
| Subjects: | Remote sensing, Spectral sensitivity, Environmental monitoring, Image segmentation, Deep learning |
| Abstract: | The semantic segmentation of remote sensing images is vital for accurate surface monitoring and environmental assessment. Multimodal remote sensing images (RSIs) provide a more comprehensive dimension of information, enabling faster and more scientific decision-making. However, existing methods primarily focus on modality and spectral channels when utilizing spectral features, with limited consideration of their association to ground object types. This association, commonly referred to as the spectral characteristics of ground objects (SCGO), results in distinct spectral responses across different modalities and holds significant potential for improving the segmentation accuracy of multimodal RSIs. Meanwhile, the inclusion of redundant features in the fusion process can also interfere with model performance. To address these problems, a ground object spectral feature awareness network (SFANet) specifically designed for RSIs that effectively leverages spectral features by incorporating the SCGO is proposed. SFANet includes two innovative modules: (1) the Spectral Aware Feature Fusion module, which integrates multimodal features in the encoder based on SCGO, and (2) the Adaptive Spectral Enhancement module, which reduces the confusion from redundant information in the decoder. SFANet significantly improves the mIoU by 5.66% and 4.76% compared to the baseline on two datasets, outperforming existing multimodal RSIs segmentation networks by adaptively enhanced spectral feature awareness. SFANet demonstrates significant advancements over other multimodal RSIs segmentation networks and provides new perspectives for RSI-specific network design by incorporating spectral characteristics. This work offers new perspectives for the design of segmentation networks for RSIs. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | The semantic segmentation of remote sensing images is vital for accurate surface monitoring and environmental assessment. Multimodal remote sensing images (RSIs) provide a more comprehensive dimension of information, enabling faster and more scientific decision-making. However, existing methods primarily focus on modality and spectral channels when utilizing spectral features, with limited consideration of their association to ground object types. This association, commonly referred to as the spectral characteristics of ground objects (SCGO), results in distinct spectral responses across different modalities and holds significant potential for improving the segmentation accuracy of multimodal RSIs. Meanwhile, the inclusion of redundant features in the fusion process can also interfere with model performance. To address these problems, a ground object spectral feature awareness network (SFANet) specifically designed for RSIs that effectively leverages spectral features by incorporating the SCGO is proposed. SFANet includes two innovative modules: (1) the Spectral Aware Feature Fusion module, which integrates multimodal features in the encoder based on SCGO, and (2) the Adaptive Spectral Enhancement module, which reduces the confusion from redundant information in the decoder. SFANet significantly improves the mIoU by 5.66% and 4.76% compared to the baseline on two datasets, outperforming existing multimodal RSIs segmentation networks by adaptively enhanced spectral feature awareness. SFANet demonstrates significant advancements over other multimodal RSIs segmentation networks and provides new perspectives for RSI-specific network design by incorporating spectral characteristics. This work offers new perspectives for the design of segmentation networks for RSIs. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs17101797 |
