HypsLiDNet: 3-D–2-D CNN Model and Spatial–Spectral Morphological Attention for Crop Classification With DESIS and LiDAR Data

The advent of cloud computing and advanced processing technologies has elevated deep learning (DL) as a leading method for hyperspectral imaging (HSI) classification. Classifying crops accurately is vital for generating precise agricultural data to support informed decision-making. This study introduces a DL framework, called HypsLiDNet, tailored for remote sensing activities. This model processes HSI in conjunction with innovative, comprehensive light detection and ranging (LiDAR) data from Hungary to conduct thorough examinations of the Earth's surface. Integrating LiDAR attributes with HSI is anticipated to enhance classification accuracy beyond HSI-only techniques. LiDAR integration provides a significant advantage by adding structural details to spectral data, aiding in the correct identification of objects with similar spectral characteristics but different shapes.The HypsLiDNet method utilizes morphological operations on LiDAR data to extract features indicative of the land's shape and texture. These features are then combined with HSI data through an attention mechanism that selectively highlights key features from both data types, improving the model's accuracy in predictions. This is particularly beneficial for complex environmental assessments, such as distinguishing between plant species. The attention mechanism also refines the feature selection process, prioritizing relevant information, which boosts computational efficiency and reduces the use of resources. Moreover, this method requires a smaller number of training samples. HypsLiDNet showcases its ability to classify with precision by harnessing the combined power of HSI and LiDAR data. Experimental results show a significant improvement in classification outcomes, outperforming traditional machine learning approaches by more than 14% and recent DL techniques by approximately 1%–3%.