研究高光谱与激光雷达融合中波段顺序的影响，提升分类精度。
HSLiNets: Evaluating Band Ordering Strategies in Hyperspectral and LiDAR Fusion
- 设计新架构，自适应融合多种波段顺序特征。
- 在休斯顿2013和特伦托数据集上准确率优于现有模型。
- 揭示波段顺序是影响融合性能的关键但被忽视的因素。
高光谱成像（HSI）与激光雷达（LiDAR）数据融合可为遥感应用提供互补的光谱与空间信息。尽管先前研究关注了高光谱中的波段选择与分组，但对波段顺序在与激光雷达融合时如何影响分类结果的关注甚少。本文系统研究了波段顺序对高光谱-激光雷达融合性能的影响。通过大量实验，我们证明波段顺序显著影响分类准确率，揭示了一个此前被忽视的融合模型关键因素。受此启发，我们提出一种新型融合架构，不仅整合高光谱与激光雷达数据，还从多个波段顺序配置中学习。该方法通过自适应融合不同光谱序列，增强特征表示，从而提升分类准确率。在休斯顿2013和特伦托数据集上的实验结果表明，该方法优于当前最先进的融合模型。数据与代码已公开于 https://github.com/Judyxyang/HSLiNets。
原文摘要 · Abstract (English)
The integration of hyperspectral imaging (HSI) and Light Detection and Ranging (LiDAR) data provides complementary spectral and spatial information for remote sensing applications. While previous studies have explored the role of band selection and grouping in HSI classification, little attention has been given to how the spectral sequence or band order affects classification outcomes when fused with LiDAR. In this work, we systematically investigate the influence of band order on HSI-LiDAR fusion performance. Through extensive experiments, we demonstrate that band order significantly impacts classification accuracy, revealing a previously overlooked factor in fusion-based models. Motivated by this observation, we propose a novel fusion architecture that not only integrates HSI and LiDAR data but also learns from multiple band order configurations. The proposed method enhances feature representation by adaptively fusing different spectral sequences, leading to improved classification accuracy. Experimental results on the Houston 2013 and Trento datasets show that our approach outperforms state-of-the-art fusion models. Data and code are available at https://github.com/Judyxyang/HSLiNets.
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