  As the field of remote sensing is evolving, we witness the accumulation of
information from several modalities, such as multispectral (MS), hyperspectral
(HSI), LiDAR etc. Each of these modalities possess its own distinct
characteristics and when combined synergistically, perform very well in the
recognition and classification tasks. However, fusing multiple modalities in
remote sensing is cumbersome due to highly disparate domains. Furthermore, the
existing methods do not facilitate cross-modal interactions. To this end, we
propose a novel transformer based fusion method for HSI and LiDAR modalities.
The model is composed of stacked auto encoders that harness the cross key-value
pairs for HSI and LiDAR, thus establishing a communication between the two
modalities, while simultaneously using the CNNs to extract the spectral and
spatial information from HSI and LiDAR. We test our model on Houston (Data
Fusion Contest - 2013) and MUUFL Gulfport datasets and achieve competitive
results.
