HSLiNets: Evaluating Band Ordering Strategies in Hyperspectral and LiDAR Fusion
This addresses a previously overlooked factor in remote sensing fusion for applications like land cover classification, but it is incremental as it builds on existing fusion methods by focusing on band ordering.
The paper tackles the problem of how band order in hyperspectral imaging (HSI) affects classification accuracy when fused with LiDAR data, finding it significantly impacts performance and proposing a novel architecture that improves accuracy by learning from multiple band orders, with experimental results showing it outperforms state-of-the-art models on Houston 2013 and Trento datasets.
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.