Semi-supervised fuzzy neighborhood preserving analysis for feature extraction in hyperspectral remote sensing images

Semi-supervised feature extraction methods are an important focus of interest in data mining and machine learning areas. These methods are improved methods based on learning from a combination of labeled and unlabeled data. In this study, a semi-supervised feature extraction method called as semi-supervised fuzzy neighborhood preserving analysis (SFNPA) is proposed to improve the classification accuracy of hyperspectral remote sensing images. The proposed method combines the principal component analysis (PCA) method, which is an unsupervised feature extraction method, and the supervised fuzzy neighborhood preserving analysis (FNPA) method and increases the classification accuracy by using a limited number of labeled data. Experimental results on four popular hyperspectral remote sensing datasets show that the proposed method significantly improves classification accuracy on hyperspectral remote sensing images compared to the well-known semi-supervised dimension reduction methods.