Kavli Affiliate: Jing Wang
| First 5 Authors: Judy X Yang, Jun Zhou, Jing Wang, Hui Tian, Wee Chung Liew
| Summary:
The fusion of hyperspectral and LiDAR data has been an active research topic.
Existing fusion methods have ignored the high-dimensionality and redundancy
challenges in hyperspectral images, despite that band selection methods have
been intensively studied for hyperspectral image (HSI) processing. This paper
addresses this significant gap by introducing a cross-attention mechanism from
the transformer architecture for the selection of HSI bands guided by LiDAR
data. LiDAR provides high-resolution vertical structural information, which can
be useful in distinguishing different types of land cover that may have similar
spectral signatures but different structural profiles. In our approach, the
LiDAR data are used as the "query" to search and identify the "key" from the
HSI to choose the most pertinent bands for LiDAR. This method ensures that the
selected HSI bands drastically reduce redundancy and computational requirements
while working optimally with the LiDAR data. Extensive experiments have been
undertaken on three paired HSI and LiDAR data sets: Houston 2013, Trento and
MUUFL. The results highlight the superiority of the cross-attention mechanism,
underlining the enhanced classification accuracy of the identified HSI bands
when fused with the LiDAR features. The results also show that the use of fewer
bands combined with LiDAR surpasses the performance of state-of-the-art fusion
models.
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