Locally Weighted Discriminant Analysis for Hyperspectral Image Classification
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 discri...
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MDPI AG
2019-01-01
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| Series: | Remote Sensing |
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| Online Access: | http://www.mdpi.com/2072-4292/11/2/109 |
| _version_ | 1819002190445936640 |
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| author | Xiaoyan Li Lefei Zhang Jane You |
| author_facet | Xiaoyan Li Lefei Zhang Jane You |
| author_sort | Xiaoyan Li |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-20T23:01:10Z |
| format | Article |
| id | doaj.art-83709a6de8524dd8aa6885062f49c1d4 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-12-20T23:01:10Z |
| publishDate | 2019-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-83709a6de8524dd8aa6885062f49c1d42022-12-21T19:23:59ZengMDPI AGRemote Sensing2072-42922019-01-0111210910.3390/rs11020109rs11020109Locally Weighted Discriminant Analysis for Hyperspectral Image ClassificationXiaoyan Li0Lefei Zhang1Jane You2School of Computer Science, China University of Geosciences, Wuhan 430074, ChinaSchool of Computer, Wuhan University, Wuhan 430072, ChinaDepartment of Computing, The Hong Kong Polytechnic University, Kowloon, Hong Kong 999077, ChinaA 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.http://www.mdpi.com/2072-4292/11/2/109hyperspectral image (HSI) classificationlinear discriminant analysis (LDA)dimensionality reductionspatial-spectral information |
| spellingShingle | Xiaoyan Li Lefei Zhang Jane You Locally Weighted Discriminant Analysis for Hyperspectral Image Classification Remote Sensing hyperspectral image (HSI) classification linear discriminant analysis (LDA) dimensionality reduction spatial-spectral information |
| title | Locally Weighted Discriminant Analysis for Hyperspectral Image Classification |
| title_full | Locally Weighted Discriminant Analysis for Hyperspectral Image Classification |
| title_fullStr | Locally Weighted Discriminant Analysis for Hyperspectral Image Classification |
| title_full_unstemmed | Locally Weighted Discriminant Analysis for Hyperspectral Image Classification |
| title_short | Locally Weighted Discriminant Analysis for Hyperspectral Image Classification |
| title_sort | locally weighted discriminant analysis for hyperspectral image classification |
| topic | hyperspectral image (HSI) classification linear discriminant analysis (LDA) dimensionality reduction spatial-spectral information |
| url | http://www.mdpi.com/2072-4292/11/2/109 |
| work_keys_str_mv | AT xiaoyanli locallyweighteddiscriminantanalysisforhyperspectralimageclassification AT lefeizhang locallyweighteddiscriminantanalysisforhyperspectralimageclassification AT janeyou locallyweighteddiscriminantanalysisforhyperspectralimageclassification |