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

• Published in: Neural computing & applications Vol. 31; no. 8; pp. 3385 - 3415
• Main Authors: Akyürek, Hasan Ali, Koçer, Barış
• Format: Journal Article
• Language: English
• Published: London Springer London 01-08-2019; Springer Nature B.V
• Subjects: Accuracy; Artificial Intelligence; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Data mining; Data Mining and Knowledge Discovery; Detection; Feature extraction; Image classification; Image Processing and Computer Vision; Machine learning; Methods; Neighborhoods; Original Article; Principal components analysis; Probability and Statistics in Computer Science; Remote sensing; Semi-supervised feature extraction; Remote sensing; Fuzzy neighborhood preserving analysis; Hyperspectral image classification
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-017-3279-y

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.