Pseudolabeling Contrastive Learning for Semisupervised Hyperspectral and LiDAR Data Classification

Abstract

Elevation information from light detection and ranging (LiDAR) data relieve the phenomenon of “same spectrum with different object” in hyperspectral images (HSI) classification. Consequently, HSI and LiDAR joint classification is a significant research topic. However, existing methods encounter several challenges. Primarily, there exists a deficiency in intraclass information interaction and underutilization of discriminative feature. Furthermore, the process of labeling samples is time-consuming and laborious. To solve the aforementioned issues, a classification method based on pseudolabeled contrastive learning is proposed to exploit substantial amounts of unlabeled information in order to enhance intraclass information interaction. The proposed method is divided into two stages for semisupervised classification. In the first stage, an unsupervised feature extraction network is designed to improve the interaction of features from multimodal data. A multimodal data cross-attention module is proposed to enhance the interaction of multimodal information at corresponding locations. Exploiting pseudolabeling contrastive learning module facilitates the interaction of information between intraclass objects. In the second stage, supervised classification with a limited number of labeled samples is performed. The multisource discriminatively consolidate feature module is designed to generate discriminative features, which are used to guide the fusion feature enhancement process. Apart from this, this module leverages multiscale features to expand the receptive field. Tested on both self-constructed and public datasets, the proposed method provides higher classification accuracy than some existing methods with a limited amount of labeled samples.

Keywords

• Contrastive learning
• feature fusion
• hyperspectral images (HSIs)
• light detection and ranging (LiDAR) data
• remote sensing
• Yellow River Delta