A Novel Dual-Encoder Model for Hyperspectral and LiDAR Joint Classification via Contrastive Learning

Deep-learning-based multi-sensor hyperspectral image classification algorithms can automatically acquire the advanced features of multiple sensor images, enabling the classification model to better characterize the data and improve the classification accuracy. However, the currently available classification methods for feature representation in multi-sensor remote sensing data in their respective domains do not focus on the existence of bottlenecks in heterogeneous feature fusion due to different sensors. This problem directly limits the final collaborative classification performance. In this paper, to address the bottleneck problem of joint classification due to the difference in heterogeneous features, we innovatively combine self-supervised comparative learning while designing a robust and discriminative feature extraction network for multi-sensor data, using spectral-spatial information from hyperspectral images (HSIs) and elevation information from LiDAR. The advantages of multi-sensor data are realized. The dual encoders of the hyperspectral encoder by the ConvNeXt network (ConvNeXt-HSI) and the LiDAR encoder by Octave Convolution (OctaveConv-LiDAR) are also used. The adequate feature representation of spectral-spatial features and depth information obtained from different sensors is performed for the joint classification of hyperspectral images and LiDAR data. The multi-sensor joint classification performance of both HSI and LiDAR sensors is greatly improved. Finally, on the Houston2013 dataset and the Trento dataset, we demonstrate through a series of experiments that the dual-encoder model for hyperspectral and LiDAR joint classification via contrastive learning achieves state-of-the-art classification performance.