Clustered Multiple Manifold Metric Learning for Hyperspectral Image Dimensionality Reduction and Classification

Dimensionality reduction (DR) technology is an important part of hyperspectral image (HSI) processing. The DR technology can effectively remove the redundant information in the HSIs and avoid the Hughes phenomenon, which is beneficial to image classification. Metric learning is widely used in DR technology, the goal of which is to achieve DR by maximizing the distance of between-class while minimizing the distance of within-class. However, traditional metric learning does not consider the intrinsic structure of data during the training process, resulting in insufficient information utilization. In order to solve the above problems, this article proposes clustered multiple manifold metric learning (CM 3 L) by combining manifold learning with metric learning for DR and classification of HSI. The proposed CM 3 L algorithm first divides the original data into multiple independent clusters and regards each sample point and its near point in the cluster as a whole, constructing it as a manifold. Then, CM 3 L uses the manifold metric distance to replace the traditional metric distance. Finally, by making full use of the local information of HSI in such a way, the discrimination ability is enhanced. Intensive experimental results on three real HSI datasets validate the effectiveness of our proposed CM 3 L algorithm.