Multimodal Colearning Meets Remote Sensing: Taxonomy, State of the Art, and Future Works
Nhi Kieu,
Kien Nguyen,
Abdullah Nazib
et al.
Abstract:In remote sensing (RS), multiple modalities of data are usually available, e.g., RGB, Multispectral, Hyperspectral, LiDAR, and SAR. Multimodal machine learning systems, which fuse these rich multimodal data modalities, have shown better performance compared to unimodal systems. Most multimodal research assumes that all modalities are present, aligned, and noiseless during training and testing time. However, in real-world scenarios, it is common to observe that one or more modalities are missing, noisy, and non… Show more
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