TY - GEN
T1 - High-Resolution Impervious Surface Area Segmentation Network Based on Multimodal Data Fusion
AU - Ma, Xiaohu
AU - Gao, Chenzhong
AU - Zhang, Mengmeng
AU - Gao, Yunhao
AU - Li, Wei
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - With accelerated urbanization, the increase in impervious surface area (ISA) leads to urban heat island effects and heightened flood risks, making accurate high-resolution ISA extraction crucial. Traditional methods face limitations, while deep learning models, particularly U-Net architectures, demonstrate superior performance. However, traditional methods relying on a single data source for ISA identification have limitations. Utilizing multimodal remote sensing data fusion can enhance the accuracy of ISA extraction. Therefore, this study proposes a multimodal data fusion and segmentation network that integrates synchronized multimodal data augmentation and a hybrid sliding window post-processing strategy to improve model generalization and robustness. The approach was validated by achieving the runner-up position in the 2025 IEEE WHISPER Multimodal Semantic Segmentation Challenge, demonstrating its effectiveness.
AB - With accelerated urbanization, the increase in impervious surface area (ISA) leads to urban heat island effects and heightened flood risks, making accurate high-resolution ISA extraction crucial. Traditional methods face limitations, while deep learning models, particularly U-Net architectures, demonstrate superior performance. However, traditional methods relying on a single data source for ISA identification have limitations. Utilizing multimodal remote sensing data fusion can enhance the accuracy of ISA extraction. Therefore, this study proposes a multimodal data fusion and segmentation network that integrates synchronized multimodal data augmentation and a hybrid sliding window post-processing strategy to improve model generalization and robustness. The approach was validated by achieving the runner-up position in the 2025 IEEE WHISPER Multimodal Semantic Segmentation Challenge, demonstrating its effectiveness.
KW - Impervious surface area
KW - multimodal fusion
KW - semantic segmentation
UR - https://www.scopus.com/pages/publications/105040835336
U2 - 10.1109/WHISPERS69515.2025.11501577
DO - 10.1109/WHISPERS69515.2025.11501577
M3 - Conference contribution
AN - SCOPUS:105040835336
T3 - 2025 15th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2025
BT - 2025 15th Workshop on Hyperspectral Imaging and Signal Processing
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2025 15th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2025
Y2 - 12 November 2025 through 14 November 2025
ER -