FusDreamer: Label-efficient Remote Sensing World Model for Multimodal Data Classification

• URL: http://arxiv.org/abs/2503.13814v1
• Date: Tue, 18 Mar 2025 01:45:51 GMT
• Title: FusDreamer: Label-efficient Remote Sensing World Model for Multimodal Data Classification
• Authors: Jinping Wang, Weiwei Song, Hao Chen, Jinchang Ren, Huimin Zhao,
• Abstract summary: This paper proposes a label-efficient remote sensing world model for multimodal data fusion (FusDreamer)<n>The FusDreamer uses the world model as a unified representation container to abstract common and high-level knowledge.<n> Experiments conducted on four typical datasets indicate the effectiveness and advantages of the proposed FusDreamer.
• Score: 7.523866920738647
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: World models significantly enhance hierarchical understanding, improving data integration and learning efficiency. To explore the potential of the world model in the remote sensing (RS) field, this paper proposes a label-efficient remote sensing world model for multimodal data fusion (FusDreamer). The FusDreamer uses the world model as a unified representation container to abstract common and high-level knowledge, promoting interactions across different types of data, \emph{i.e.}, hyperspectral (HSI), light detection and ranging (LiDAR), and text data. Initially, a new latent diffusion fusion and multimodal generation paradigm (LaMG) is utilized for its exceptional information integration and detail retention capabilities. Subsequently, an open-world knowledge-guided consistency projection (OK-CP) module incorporates prompt representations for visually described objects and aligns language-visual features through contrastive learning. In this way, the domain gap can be bridged by fine-tuning the pre-trained world models with limited samples. Finally, an end-to-end multitask combinatorial optimization (MuCO) strategy can capture slight feature bias and constrain the diffusion process in a collaboratively learnable direction. Experiments conducted on four typical datasets indicate the effectiveness and advantages of the proposed FusDreamer. The corresponding code will be released at https://github.com/Cimy-wang/FusDreamer.

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