Abstract
| Original language | English |
|---|---|
| Pages (from-to) | 4418-4433 |
| Number of pages | 16 |
| Journal | IEEE Transactions on Geoscience and Remote Sensing |
| Volume | 53 |
| Issue number | 8 |
| Early online date | 20 Feb 2015 |
| DOIs | |
| Publication status | Published - 31 Aug 2015 |
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Keywords
- data classification
- feature extraction
- hyperspectral imaging
- 2-D empirical mode decomposition
- 2-D singular spectrum analysis
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Novel two dimensional singular spectrum analysis for effective feature extraction and data classification in hyperspectral imaging. / Zabalza, Jaime; Ren, Jinchang; Zheng, Jiangbin; Han, Junwei; Zhao, Huimin; Li, Shutao; Marshall, Stephen.
In: IEEE Transactions on Geoscience and Remote Sensing, Vol. 53, No. 8, 31.08.2015, p. 4418-4433.Research output: Contribution to journal › Article
TY - JOUR
T1 - Novel two dimensional singular spectrum analysis for effective feature extraction and data classification in hyperspectral imaging
AU - Zabalza, Jaime
AU - Ren, Jinchang
AU - Zheng, Jiangbin
AU - Han, Junwei
AU - Zhao, Huimin
AU - Li, Shutao
AU - Marshall, Stephen
N1 - (c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works
PY - 2015/8/31
Y1 - 2015/8/31
N2 - Feature extraction is of high importance for effective data classification in hyperspectral imaging (HSI). Considering the high correlation among band images, spectral-domain feature extraction is widely employed. For effective spatial information extraction, a 2-D extension to singular spectrum analysis (SSA), a recent technique for generic data mining and temporal signal analysis, is proposed. With 2D-SSA applied to HSI, each band image is decomposed into varying trend, oscillations and noise. Using the trend and selected oscillations as features, the reconstructed signal, with noise highly suppressed, becomes more robust and effective for data classification. Three publicly available data sets for HSI remote sensing data classification are used in our experiments. Comprehensive results using a support vector machine (SVM) classifier have quantitatively evaluated the efficacy of the proposed approach. Benchmarked with several state-of-the-art methods including 2-D empirical mode decomposition (2D-EMD), it is found that our proposed 2D-SSA approach generates the best results in most cases. Unlike 2D-EMD which requires sequential transforms to obtain detailed decomposition, 2D-SSA extracts all components simultaneously. As a result, the executive time in feature extraction can also be dramatically reduced. The superiority in terms of enhanced discrimination ability from 2D-SSA is further validated when a relatively weak classifier, k-nearest neighbor (k-NN), is used for data classification. In addition, the combination of 2D-SSA with 1D-PCA (2D-SSA-PCA) has generated the best results among several other approaches, which has demonstrated the great potential in combining 2D-SSA with other approaches for effective spatial-spectral feature extraction and dimension reduction in HSI.
AB - Feature extraction is of high importance for effective data classification in hyperspectral imaging (HSI). Considering the high correlation among band images, spectral-domain feature extraction is widely employed. For effective spatial information extraction, a 2-D extension to singular spectrum analysis (SSA), a recent technique for generic data mining and temporal signal analysis, is proposed. With 2D-SSA applied to HSI, each band image is decomposed into varying trend, oscillations and noise. Using the trend and selected oscillations as features, the reconstructed signal, with noise highly suppressed, becomes more robust and effective for data classification. Three publicly available data sets for HSI remote sensing data classification are used in our experiments. Comprehensive results using a support vector machine (SVM) classifier have quantitatively evaluated the efficacy of the proposed approach. Benchmarked with several state-of-the-art methods including 2-D empirical mode decomposition (2D-EMD), it is found that our proposed 2D-SSA approach generates the best results in most cases. Unlike 2D-EMD which requires sequential transforms to obtain detailed decomposition, 2D-SSA extracts all components simultaneously. As a result, the executive time in feature extraction can also be dramatically reduced. The superiority in terms of enhanced discrimination ability from 2D-SSA is further validated when a relatively weak classifier, k-nearest neighbor (k-NN), is used for data classification. In addition, the combination of 2D-SSA with 1D-PCA (2D-SSA-PCA) has generated the best results among several other approaches, which has demonstrated the great potential in combining 2D-SSA with other approaches for effective spatial-spectral feature extraction and dimension reduction in HSI.
KW - data classification
KW - feature extraction
KW - hyperspectral imaging
KW - 2-D empirical mode decomposition
KW - 2-D singular spectrum analysis
U2 - 10.1109/TGRS.2015.2398468
DO - 10.1109/TGRS.2015.2398468
M3 - Article
VL - 53
SP - 4418
EP - 4433
JO - IEEE Transactions on Geoscience and Remote Sensing
JF - IEEE Transactions on Geoscience and Remote Sensing
SN - 0196-2892
IS - 8
ER -