A deep feature manifold embedding method for hyperspectral image classification. Issue 7 (2nd July 2020)

• Record Type: Journal Article
• Authors: Liu, Jiamin; Yang, Song; Huang, Hong; Li, Zhengying; Shi, Guangyao
• Is Part Of: Remote sensing letters. Volume 11:Issue 7(2020)
• Page Start: 620
• Page End: 629
• Publication Date: 2020-07-02
• Subjects: Remote sensing -- Periodicals; Remote sensing; Periodicals; 621.3678
• DOI: 10.1080/2150704X.2020.1746855
• Languages: English
• ISSNs: 2150-704X

Abstract

ABSTRACT: In this letter, we proposed a novel deep feature manifold embedding method to improve feature extraction ability of traditional deep learning methods. This method first obtains deep features of hyperspectral image (HSI) from a trained autoencoder. Then, an intrinsic graph and a penalty graph are constructed to discover the discriminant manifold structure of deep features. Finally, the deep features are mapped into a low-dimensional embedding space, in which samples in intraclass manifold are compacted and samples from interclass manifolds are separated. Experiments on Pavia University, Indian Pines and Urban datasets demonstrate that the proposed method effectively improves the classification performance of HSI compared with other state-of-the-art approaches.