Frequency-Domain Feature Enhancement Combined with Contrastive Learning for Hyperspectral Image Open-Set Classification

Hyperspectral image classification typically assumes that training and test data belong to the same set of categories. However, in practical applications, unknown categories of ground objects frequently arise during the testing phase, a scenario known as open-set classification. In open-set classification, the high spectral similarity between known and unknown classes makes it challenging to effectively capture subtle differences in the frequency domain using only spatial or low-frequency features. This difficulty in delineating the decision boundary limits the effective rejection of unknown classes. We propose an open-set classification method for hyperspectral images that combines frequency domain feature enhancement with contrastive learning. First, we construct a wavelet transform enhancement model to decompose spectral features into high- and low-frequency components, capturing subtle spectral differences for refined representation. Second, we design a multi-scale feature fusion ResNet that assigns prior importance to the convolution kernel and recalibrates it using weighted convolution. This enhances the model’s ability to capture fine-grained spatial features and improves the distinction of category boundaries. Finally, we combine dynamic hard sample mining with contrastive learning. Through a progressive strategy that ensures model training stability, we achieve deeper mining and accurate identification of difficult boundary samples, thereby improving the accuracy of unknown category classification. Experimental results on the Houston 2013, GF-5 Yancheng, and Salinas datasets show that this method achieves overall classification accuracies of 90.16%, 97.24%, and 91.07%, respectively. These results outperform mainstream open-set methods, such as DTCL and CACL, fully demonstrating the method’s effectiveness and superiority in open-set classification tasks.