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Remote Sens. 2017, 9(8), 790; doi:10.3390/rs9080790

Local Geometric Structure Feature for Dimensionality Reduction of Hyperspectral Imagery

1
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
2
Key Laboratory of Optoelectronic Technology and Systems of the Education Ministry of China, Chongqing University, Chongqing 400044, China
3
Department of Mechanical Engineering, Sichuan University of Science and Engineering, Zigong 643000, China
*
Author to whom correspondence should be addressed.
Academic Editors: Yanfei Zhong and Prasad S. Thenkabail
Received: 10 July 2017 / Revised: 24 July 2017 / Accepted: 29 July 2017 / Published: 1 August 2017
View Full-Text   |   Download PDF [6165 KB, uploaded 1 August 2017]   |  

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 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
Keywords: hyperspectral imagery; dimensionality reduction; manifold learning; local geometric structure; marginal Fisher analysis hyperspectral imagery; dimensionality reduction; manifold learning; local geometric structure; marginal Fisher analysis
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MDPI and ACS Style

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.

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