用双路非线性网络融合高光谱与激光雷达数据，提升分类精度并降低计算开销。
HSLiNets: Hyperspectral Image and LiDAR Data Fusion Using Efficient Dual Non-Linear Feature Learning Networks
- 设计双反向卷积路径+空间分析模块，实现光谱与空间特征高效融合。
- 在休斯顿2013数据集上优于现有先进模型，分类准确率显著提升。
- 适合资源受限的遥感场景，兼顾精度与计算效率，适合实际部署。
将高光谱影像（HSI）与激光雷达（LiDAR）数据融合于新的线性特征空间，为解决高维性和冗余性问题提供了有效途径。本研究提出一种双线性融合空间框架，采用双向反向卷积神经网络（CNN）路径与专用空间分析模块，结合CNN的计算效率与注意力机制的适应性，有效融合光谱与空间信息。该方法不仅提升了数据处理速度和分类准确率，还缓解了如Transformer等复杂模型带来的高计算负担。在休斯顿2013数据集上的评估表明，该方法超越现有最先进模型。这一进展凸显了该框架在资源受限环境中的潜力，对遥感领域具有重要意义。
原文摘要 · Abstract (English)
The integration of hyperspectral imaging (HSI) and LiDAR data within new linear feature spaces offers a promising solution to the challenges posed by the high-dimensionality and redundancy inherent in HSIs. This study introduces a dual linear fused space framework that capitalizes on bidirectional reversed convolutional neural network (CNN) pathways, coupled with a specialized spatial analysis block. This approach combines the computational efficiency of CNNs with the adaptability of attention mechanisms, facilitating the effective fusion of spectral and spatial information. The proposed method not only enhances data processing and classification accuracy, but also mitigates the computational burden typically associated with advanced models such as Transformers. Evaluations of the Houston 2013 dataset demonstrate that our approach surpasses existing state-of-the-art models. This advancement underscores the potential of the framework in resource-constrained environments and its significant contributions to the field of remote sensing.
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