HSLiNets: Hyperspectral Image and LiDAR Data Fusion Using Efficient Dual Non-Linear Feature Learning Networks
This work addresses data fusion problems in remote sensing for applications like environmental monitoring, but it is incremental as it builds on existing CNN and attention methods.
The paper tackles the challenge of high-dimensionality and redundancy in hyperspectral imaging by fusing it with LiDAR data using a dual linear fused space framework with bidirectional CNN pathways and a spatial analysis block, achieving improved classification accuracy and computational efficiency on the Houston 2013 dataset.
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.