Paper Info
Title
Dimensionality reduction of hyperspectral images with local geometric structure Fisher analysis
Abstract
Marginal 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 relationship of neighbor points, and it cannot effectively represent the intrinsic structure of hyperspectral image (HSI) that possesses many homogenous areas. In this paper, we proposed a new dimensionality reduce (DR) model, termed local geometric structure Fisher analysis (LGSFA), for HSI classification. At first, this method computes the reconstruction point of each point with its intraclass neighbor points. Then, an intrinsic graph and a penalty graph are constructed to reveal the intraclass and interclass relationship, respectively. Finally, the neighbor points and corresponding reconstruction points are used to enhance the intraclass compactness and interclass separability in low-dimensional space. LGSFA can effectively reveal the intrinsic manifold structure and obtains the discriminating feature of HSI data. Experiments on Salinas HSI data set show that the proposed LGSFA algorithm performs the best classification results than other state-of-the-art methods.
Year
DOI
Venue
2016
10.1109/IGARSS.2016.7729004
2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
Keywords
Field
DocType
Hyperspectral image,dimensionality reduction,manifold learning,local geometric structure,Fisher analysis
Iterative reconstruction,Computer vision,Algorithm design,Dimensionality reduction,Pattern recognition,Computer science,Compact space,Hyperspectral imaging,Curse of dimensionality,Artificial intelligence,Manifold,Principal component analysis
Conference
ISSN
ISBN
Citations 
2153-6996
978-1-5090-3333-1
0
PageRank 
References 
Authors
0.34
7
4
Name
Order
Citations
PageRank
Fulin Luo1345.85
Hong Huang2284.59
Yaqiong Yang300.34
Zhiyong Lv400.34