Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/80679
Title: Locally weighted discriminant analysis for hyperspectral image classification
Authors: Li, X
Zhang, L
You, J 
Keywords: Dimensionality reduction
Hyperspectral image (HSI) classification
Linear discriminant analysis (LDA)
Spatial-spectral information
Issue Date: 2019
Publisher: Molecular Diversity Preservation International (MDPI)
Source: Remote sensing, 2019, v. 11, no. 2, 109 How to cite?
Journal: Remote sensing 
Abstract: A hyperspectral image (HSI) contains a great number of spectral bands for each pixel, which will limit the conventional image classification methods to distinguish land-cover types of each pixel. Dimensionality reduction is an effective way to improve the performance of classification. Linear discriminant analysis (LDA) is a popular dimensionality reduction method for HSI classification, which assumes all the samples obey the same distribution. However, different samples may have different contributions in the computation of scatter matrices. To address the problem of feature redundancy, a new supervised HSI classification method based on locally weighted discriminant analysis (LWDA) is presented. The proposed LWDA method constructs a weighted discriminant scatter matrix model and an optimal projection matrix model for each training sample, which is on the basis of discriminant information and spatial-spectral information. For each test sample, LWDA searches its nearest training sample with spatial information and then uses the corresponding projection matrix to project the test sample and all the training samples into a low-dimensional feature space. LWDA can effectively preserve the spatial-spectral local structures of the original HSI data and improve the discriminating power of the projected data for the final classification. Experimental results on two real-world HSI datasets show the effectiveness of the proposed LWDA method compared with some state-of-the-art algorithms. Especially when the data partition factor is small, i.e., 0.05, the overall accuracy obtained by LWDA increases by about 20% for Indian Pines and 17% for Kennedy Space Center (KSC) in comparison with the results obtained when directly using the original high-dimensional data.
URI: http://hdl.handle.net/10397/80679
EISSN: 2072-4292
DOI: 10.3390/rs11020109
Rights: © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
The following publication Li X, Zhang L, You J. Locally Weighted Discriminant Analysis for Hyperspectral Image Classification. Remote Sensing. 2019; 11(2):109 is available at https://doi.org/10.3390/rs11020109
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