HSLiNets: Hyperspectral Image and LiDAR Data Fusion Using Efficient Dual Non-Linear Feature Learning Networks

TL;DR

This paper presents HSLiNets, a novel dual non-linear feature learning network that efficiently fuses hyperspectral and LiDAR data, improving classification accuracy while reducing computational costs.

Contribution

It introduces a dual linear fused space framework with bidirectional CNN pathways and attention mechanisms for effective spectral and spatial data integration.

Findings

Outperforms existing state-of-the-art models on Houston 2013 dataset

Enhances data processing and classification accuracy

Reduces computational burden compared to Transformer-based models

Abstract

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.