Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/76422
Title: Exploring locally adaptive dimensionality reduction for hyperspectral image classification : a maximum margin metric learning aspect
Authors: Dong, YN
Du, B
Zhang, LP
Zhang, LF 
Keywords: Dimensionality reduction
Hyperspectral image classification
Locally adaptive decision constraints
Metric learning
Issue Date: 2017
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE journal of selected topics in applied earth observations and remote sensing, 2017, v. 10, no. 3, p. 1136-1150 How to cite?
Journal: IEEE journal of selected topics in applied earth observations and remote sensing 
Abstract: The high-dimensional data space generated by hyperspectral sensors introduces challenges for the conventional data analysis techniques. Popular dimensionality reduction techniques usually assume a Gaussian distribution, which may not be in accordance with real life. Metric learning methods, which explore the global data structure of the labeled training samples, have proved to be very efficient in hyperspectral fields. However, we can go further by utilizing locally adaptive decision constraints for the labeled training samples per class to obtain an even better performance. In this paper, we present the locally adaptive dimensionality reduction metric learning (LADRml) method for hyperspectral image classification. The aims of the presented method are: 1) first, to utilize the limited training samples to reduce the dimensionality of data without a certain distribution hypothesis; and 2) second, to better handle data with complex distributions by the use of locally adaptive decision constraints, which can assess the similarity between a pair of samples based on the distance changes before and after metric learning. The experimental results obtained with a number of challenging hyperspectral image datasets demonstrate that the proposed LADRml algorithm outperforms the state-of-the-art dimensionality reduction and metric learning methods.
URI: http://hdl.handle.net/10397/76422
ISSN: 1939-1404
EISSN: 2151-1535
DOI: 10.1109/JSTARS.2016.2587747
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