Hyperspectral Image Classification Algorithm Based on Textual-Spectral Feature Joint Learning
Hyperspectral image classification is one of the most important topics in earth observation.In order to tackle the challenges of hyperspectral images with wide coverage,diverse ground features,and high difficulty in manual labeling,a hyperspectral image classification algorithm based on joint learning of textual-spectral features was desighed.The algorithm leverages the semantic priors of the textual modality to enhance the knowledge transfer capability across different scenes,utilized feature reconstruction to learn discriminative and transferable information,and employed an adaptive textual-embedding interactive learning module to explore the latent features of the encoder,which finally achieved joint optimization of multi-modal features and improves classification performance.At the same time,four different algorithms were used for comparison and verification,and the results showed that the new algorithm was superior to other algorithms in terms of single-class accuracy,overall accuracy(OA)and Kappa coefficient.
万方数据
维普
