Multimodal Land Use Classification: Harnessing HSI and LiDAR Integration

Recently, the integration of multiple remote sensing modalities has gained significant attention in land use classification research, offering improved performance. However, this approach comes with additional challenges such as modality-specific feature extraction and effective feature fusion. In this work, a DL-based technique is proposed that utilizes dual remote sensing modalities (HSI and LiDAR) for land use classification. The proposed technique consists of three modules: 1) a CNN-based feature extraction module, 2) Attention modules designed specifically for each modality, i.e., Convolution Block Attention Module (CBAM) and a spatial attention module for the HSI and the LiDAR features respectively. 3) A fusion module to fuse separately extracted features of both modalities. The features extracted from convolution blocks are subsequently enhanced using attention modules, later, feature-level fusion is performed, and final classification is achieved. The novel combination of these modules has demonstrated a notable performance gain over the CNN-based approaches across different classes and metrics on the Trento dataset. It achieves 98.21% average accuracy on the Trento dataset, which shows its significant potential to be applied in resource management and planning and environmental monitoring.