Coarse-to-Fine Complementary Representation Network for Joint Hyperspectral and LiDAR Classification

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Abstract
The fusion of hyperspectral images data (HSI) and Light Detection and Ranging data (LiDAR) has long been a research hotspot in remote sensing. Nevertheless, most existing fusion methods directly take HSI and LiDAR data as model inputs, without adequately addressing the spectral redundancy caused by the high-dimensional correlations inherent in HSI, or the cross-modal spatial redundancy induced by structural overlap in complex HSI and LiDAR datasets. In addition, although spectral, spatial, and other attribute features have been extensively explored, the latent and complex relationships among features remain insufficiently modeled. To overcome these, this paper proposes a coarse-to-fine collaborative representation network (CFCRNet). In the coarse-grained feature learning stage, Spatial Reconstruction Module models the discriminability score of spatial positions within a single channel and derives a spatial attention weight formula based on this score, which can be reused across all channels for spatial information reconstruction. Spectral Redundancy Removal Module integrates spectral grouping and spectral normalization to reduce global spectral band correlations, and focuses on suppressing redundant components in local spectral variations, thereby facilitating efficient subsequent feature fusion. In the fine-grained feature learning stage, Multi-Head Factorized Attention Fusion Module focuses on selecting and fusing common and distinctive features, and explicitly models feature dependencies to enhance the discriminability of fused representations. Based on an integrated “redundancy removal-feature learning” mechanism, the proposed model can capture more effective complementary features under complex land-cover backgrounds. Experimental evaluations on three publicly available datasets show that CFCRNet outperforms state-of-the-art methods in land-cover classification tasks.
Funding Information
  • Natural Science Foundation of Heilongjiang Province (LH2023F050)
  • Fundamental Research Funds in Heilongjiang provincial universities (145309208)

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