  Multi-source remote sensing data classification has emerged as a prominent
research topic with the advancement of various sensors. Existing multi-source
data classification methods are susceptible to irrelevant information
interference during multi-source feature extraction and fusion. To solve this
issue, we propose a sparse focus network for multi-source data classification.
Sparse attention is employed in Transformer block for HSI and SAR/LiDAR feature
extraction, thereby the most useful self-attention values are maintained for
better feature aggregation. Furthermore, cross-attention is used to enhance
multi-source feature interactions, and further improves the efficiency of
cross-modal feature fusion. Experimental results on the Berlin and Houston2018
datasets highlight the effectiveness of SF-Net, outperforming existing
state-of-the-art methods.
