Urban Functional Zone Classification Based on High-Resolution Remote Sensing Imagery and Nighttime Light Imagery

作者：Junyu Chen, Yingbiao Chen, Zihao Zheng, Zhenxiang Ling, Xianxin Meng, Junyu Kuang, Xianghua Shi, Yifan Yang, Wentao Chen, Zhifeng Wu · 发表于：Remote Sensing · 年份：2025 · DOI：10.3390/rs17091588 · 被引用次数：6 · 研究领域：Impact of Light on Environment and Health、Remote Sensing and Land Use、Remote-Sensing Image Classification

Urbanization has led to rapid changes in the landscapes of cities, making the quick and accurate identification of urban functional zones crucial for urban development. Identifying urban functional zones requires understanding not only the physical characteristics of a city but also its social attributes. However, traditional methods relying on single-modal features for classification struggle to ensure accuracy, posing challenges for subsequent fine-grained urban studies. To address the limitations of single-modal models, this study proposes an end-to-end Cross-modal Spatial Alignment Gated Fusion Deep Neural Network (CSAGFNet). This model extracts information from high-resolution remote sensing imagery and nighttime light imagery to classify urban functional zones. The CSAGFNet aligns features from different modalities using a cross-modal spatial alignment module, ensuring consistency in the same spatial dimension. Following this, a gated fusion mechanism dynamically controls the weighted integration of modal features, optimizing their interaction. In tests, CSAGFNet achieved a mean intersection over union (mIoU) value of 0.853, outperforming single-modal models by at least 5% and significantly demonstrating its superiority. Extensive ablation experiments validated the effectiveness of the core components of CSAGFNet.