Deep Dynamic Adaptation Network Based on Joint Correlation Alignment for Cross-Scene Hyperspectral Image Classification
Chong Li, Weiwei Sun, Jiangtao Peng, Jiancheng Li
Abstract
Deep learning methods face significant challenges in practical cross-scene classification tasks of hyperspectral images, primarily due to the difficulty of acquiring labels and the issue of inconsistent distribution caused by spectral drift. To tackle the above issues, we propose a deep dynamic adaptation network based on joint correlation alignment (DDAN-JCA) for cross-scene hyperspectral image classification. First, the dual-channel residual network (DCRN) and the attention mechanism module (AMM) are employed to extract spatial-spectral joint features from both source domain and target domain. Then, the method of correlation alignment (CORAL) is employed to minimize the marginal distribution discrepancy between two domains and further reduce the conditional distribution discrepancy of each class. Finally, a dynamic distribution adaptation strategy is used to dynamically adjust the importance of marginal distribution and conditional distribution by using a balance factor. DDAN-JCA can achieve unsupervised classification without using target labels. The performance of DDAN-JCA has been validated using three hyperspectral datasets, and the experimental results demonstrate that DDAN-JCA significantly enhances classification accuracy and exhibits greater robustness compared to state-of-the-art methods.