Local Geometric Structure Feature for Dimensionality Reduction of Hyperspectral Imagery
AbstractMarginal Fisher analysis (MFA) exploits the margin criterion to compact the intraclass data and separate the interclass data, and it is very useful to analyze the high-dimensional data. However, MFA just considers the structure relationships of neighbor points, and it cannot effectively represent the intrinsic structure of hyperspectral imagery (HSI) that possesses many homogenous areas. In this paper, we propose a new dimensionality reduction (DR) method, termed local geometric structure Fisher analysis (LGSFA), for HSI classification. Firstly, LGSFA uses the intraclass neighbor points of each point to compute its reconstruction point. Then, an intrinsic graph and a penalty graph are constructed to reveal the intraclass and interclass properties of hyperspectral data. Finally, the neighbor points and corresponding intraclass reconstruction points are used to enhance the intraclass-manifold compactness and the interclass-manifold separability. LGSFA can effectively reveal the intrinsic manifold structure and obtain the discriminating features of HSI data for classification. Experiments on the Salinas, Indian Pines, and Urban data sets show that the proposed LGSFA algorithm achieves the best classification results than other state-of-the-art methods. View Full-Text
Scifeed alert for new publicationsNever miss any articles matching your research from any publisher
- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
Luo, F.; Huang, H.; Duan, Y.; Liu, J.; Liao, Y. Local Geometric Structure Feature for Dimensionality Reduction of Hyperspectral Imagery. Remote Sens. 2017, 9, 790.
Luo F, Huang H, Duan Y, Liu J, Liao Y. Local Geometric Structure Feature for Dimensionality Reduction of Hyperspectral Imagery. Remote Sensing. 2017; 9(8):790.Chicago/Turabian Style
Luo, Fulin; Huang, Hong; Duan, Yule; Liu, Jiamin; Liao, Yinghua. 2017. "Local Geometric Structure Feature for Dimensionality Reduction of Hyperspectral Imagery." Remote Sens. 9, no. 8: 790.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.