# Sparse Focus Network for Multi-Source Remote Sensing Data Classification

arXiv: https://arxiv.org/abs/2406.01245 · HTML: https://mlsanity.com/paper/2406.01245/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jun 2024.

## Built on by (most cited first, 1 of 1)

- [From Stochastic Answers to Verifiable Reasoning: Interpretable Decision-Making with LLM-Generated Code](https://mlsanity.com/paper/2603.13287/) (2026)

## Abstract (arXiv)

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.