  With the convenient availability of remote sensing data, how to make models
to interpret complex remote sensing data attracts wide attention. In remote
sensing data, hyperspectral images contain spectral information and LiDAR
contains elevation information. Hence, more explorations are warranted to
better fuse the features of different source data. In this paper, we introduce
semantic understanding to dynamically fuse data from two different sources,
extract features of HSI and LiDAR through different capsule network branches
and improve self-supervised loss and random rigid rotation in Canonical Capsule
to a high-dimensional situation. Canonical Capsule computes the capsule
decomposition of objects by permutation-equivariant attention and the process
is self-supervised by training pairs of randomly rotated objects. After fusing
the features of HSI and LiDAR with semantic understanding, the unsupervised
extraction of spectral-spatial-elevation fusion features is achieved. With two
real-world examples of HSI and LiDAR fused, the experimental results show that
the proposed multi-branch high-dimensional canonical capsule algorithm can be
effective for semantic understanding of HSI and LiDAR. It indicates that the
model can extract HSI and LiDAR data features effectively as opposed to
existing models for unsupervised extraction of multi-source RS data.
