DF4LCZ：一种SAM赋能的场景级局地气候分区分类数据融合框架
DF4LCZ: A SAM-Empowered Data Fusion Framework for Scene-Level Local Climate Zone Classification
AI总结：
针对场景级局地气候分区分类中地面物体先验整合不足与数据源细节捕捉困难的问题，提出DF4LCZ双流融合框架，结合谷歌影像实例特征与Sentinel-2光谱特征，利用SAM赋能的GCN和3D-CNN提升分类性能。
摘要：
近年来，遥感技术的进步展示了其在准确分类局地气候分区（LCZs）方面的潜力。然而，传统的使用卷积神经网络（CNNs）的场景级方法往往难以有效整合地面物体的先验知识。此外，常用的数据源如Sentinel-2在捕捉详细的地面物体信息方面遇到困难。为应对这些挑战，我们提出了一种数据融合方法，将高分辨率谷歌影像中提取的地面物体先验与Sentinel-2多光谱影像相结合。该方法引入了一种新颖的双流融合框架用于LCZ分类（DF4LCZ），整合了来自谷歌影像的基于实例的位置特征与从Sentinel-2影像提取的场景级空间-光谱特征。该框架包含一个由Segment Anything Model（SAM）赋能的图卷积网络（GCN）模块，以增强从谷歌影像中的特征提取。同时，该框架采用3D-CNN架构来学习Sentinel-2影像的光谱-空间特征。实验在专门为LCZ分类设计的多源遥感图像数据集上进行，验证了所提出的DF4LCZ的有效性。相关代码和数据集可在https://github.com/ctrlovefly/DF4LCZ获取。
英文摘要：
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.
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