A Prototype Network for Hyperspectral Image Open-Set Classification Based on Feature Invariance and Weighted Pearson Distance Measurement

This study investigates the use of hyperspectral images (HSIs) in remote sensing technology, focusing on the challenges of open-set classification (OSC). The high dimensionality and complexity of HSI bring unparalleled depth and precision to remote sensing, yet pose significant classification challenges. To address these challenges, we introduce a novel prototype network based on feature invariance for open-set HSI classification (FIWPPN). This network utilizes a residual network (ResNet) architecture to extract spectral-spatial features and includes an invariance clustering module to enhance feature boundary delineation in the prototype network classification. Furthermore, we have developed a weighted Pearson distance metric to establish a measurement domain between unlabeled data and training data, facilitating open-set recognition. Experimental validation on three publicly accessible I datasets demonstrates that our method surpasses existing classification techniques in terms of classification accuracy and OSC performance.