DF4LCZ: A SAM-Empowered Data Fusion Framework for Scene-Level Local Climate Zone Classification
arxiv(2024)
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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