Recent advancements in remote sensing (RS) technologies have shown their potential in accurately classifying local climate zones (LCZs). However, traditional scene-level methods using convolutional neural networks (CNNs) often struggle to integrate prior knowledge of ground objects effectively. Moreover, commonly utilized data sources like Sentinel-2 encounter difficulties in capturing detailed ground object information. To tackle these challenges, we propose a data fusion method that integrates ground object priors extracted from high-resolution Google imagery with Sentinel-2 multispectral imagery. The proposed method introduces a novel Dual-stream Fusion framework for LCZ classification (DF4LCZ), integrating instance-based location features from Google imagery with the scene-level spatial-spectral features extracted from Sentinel-2 imagery. The framework incorporates a Graph Convolutional Network (GCN) module empowered by the Segment Anything Model (SAM) to enhance feature extraction from Google imagery. Simultaneously, the framework employs a 3D-CNN architecture to learn the spectral-spatial features of Sentinel-2 imagery. Experiments are conducted on a multi-source remote sensing image dataset specifically designed for LCZ classification, validating the effectiveness of the proposed DF4LCZ. The related code and dataset are available at https://github.com/ctrlovefly/DF4LCZ.

翻译：近年来，遥感技术的发展展示了其在准确分类局部气候分区（LCZ）方面的潜力。然而，传统的基于卷积神经网络（CNN）的场景级方法往往难以有效整合地物先验知识。此外，常用的数据源（如哨兵二号）在捕捉精细地物信息方面存在困难。为解决这些挑战，我们提出了一种数据融合方法，该方法将高分辨率谷歌影像中提取的地物先验知识与哨兵二号多光谱影像相结合。所提方法引入了一种新颖的双流融合框架用于LCZ分类（DF4LCZ），该框架整合了谷歌影像中基于实例的位置特征与哨兵二号影像中提取的场景级空间-光谱特征。该框架集成了基于“分割一切模型”（SAM）的图卷积网络（GCN）模块，以增强谷歌影像的特征提取能力。同时，框架采用三维卷积神经网络（3D-CNN）架构学习哨兵二号影像的光谱-空间特征。我们在一个专门为LCZ分类设计的多源遥感图像数据集上进行了实验，验证了所提DF4LCZ的有效性。相关代码及数据集可在https://github.com/ctrlovefly/DF4LCZ获取。