CPFENet: center pixel features enhancement network for classification of hyperspectral and LiDAR data

The joint use of information from hyperspectral image (HSI) data and light detection and ranging (LiDAR) data enables the effective identification and classification of complex land cover categories. Recently, many deep learning classification methods based on the combination of convolutional neural networks (CNN) and Transformer have been used in HSI and LiDAR classification task. However, existing algorithms pay little attention to the fusion of centre pixel features in multimodal data. In this article, we propose a novel centre pixel features enhancement network (CPFENet) for effective cross-modal information fusion. Specifically, this article introduces a centre pixel enhancement module (CPEM), which integrates absolute positional encoding and centre relative position encoding to enhance the expression of centre pixel features. Subsequently, the enhanced features are fed into a novel centre pixel feature cross-attention (CPFCA) module for fusion, which enables the network to focus on the centre pixel to learn spatial and spectral features. Experimental results on three public datasets demonstrate that the performance of the proposed network is better than that of previous state-of-the-art methods.