Connection Center Evolution and Local Similarity-Based Data Gravitation Integrated Classification Model for Effective Classification of Hyperspectral Images

Suffering from the well-known Hughes phenomenon, hyperspectral image (HSI) classification is still very challenging, which is mainly due to the high-dimensional features of HSIs and relatively limited training samples. To systematically represent the spatial and spectral relationships among the HSI data, a connection center evolution (CCE) and local similarity-based data gravitation integrated classification (CCE-LSDGC) model is proposed for the classification of HSIs. In the first step, the cosine similarity matrix of the labeled samples and their neighboring pixels is integrated with the CCE theory to enlarge the size of the training set. Then, the cosine similarity matrix of the test pixel and its neighboring pixels is used to define their local spectral and spatial similarity. The similarity is taken as the local mass of neighbors that weight the contribution of different neighbors in a data gravitation model. This effectively alleviates the interference of local heterogeneous pixels and noise. Finally, each test pixel is labeled to the class whose training samples exerted the largest average data gravitation in the local joint region. Comprehensive experiments conducted on two benchmark and two real-world HSIs datasets have verified the superiority of the CCE-LSDGC model compared to a few state-of-the-art deep learning methods. In particular, the proposed method shows high performance on the HSIs with limited training samples.