|
|
Generating labeled samples for hyperspectral image classification using correlation of spectral bands |
Lu YU1,2,Jun XIE3,Songcan CHEN2,*( ),Lei ZHU1 |
1. Institute of Communications Engineering, PLA University of Science and Technology, Nanjing 210007, China 2. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China 3. College of Command Information System, PLA University of Science and Technology, Nanjing 210007, China |
|
|
Abstract Because the labor needed to manually label a huge training sample set is usually not available, the problem of hyperspectral image classification often suffers from a lack of labeled training samples. At the same time, hyperspectral data represented in a large number of bands are usually highly correlated. In this paper, to overcome the small sample problem in hyperspectral image classification, correlation of spectral bands is fully utilized to generate multiple new sub-samples from each original sample. The number of labeled training samples is thus increased several times. Experiment results demonstrate that the proposed method has an obvious advantage when the number of labeled samples is small.
|
Keywords
hyperspectral image
remote sensing
image classification
small sample problem
|
Corresponding Author(s):
Songcan CHEN
|
Just Accepted Date: 15 June 2015
Issue Date: 16 March 2016
|
|
1 |
Zhong Y, Zhang L. An adaptive artificial immune network for supervised classification of multi-hyperspectral remote sensing imagery. IEEE Transactions on Geoscience and Remote Sensing, 2011, 50(3): 894–909
https://doi.org/10.1109/TGRS.2011.2162589
|
2 |
Pushmeet K, Lubor L, Philip H. Robust higher order potentials for enforcing label consistency. International Journal of Computer Vision, 2009, 82(3): 302–324
https://doi.org/10.1007/s11263-008-0202-0
|
3 |
Lubor L. Global structured models towards scene understanding. Dissertation for the Doctoral Degree. Oxford: Oxford Brooks Univerty, 2011
|
4 |
Yang Y, Wu F, Nie F, Shen H, Zhuang Y, Alexander G. Web and personal image annotation by mining label correlation with relaxed visual graph embedding. IEEE Transactions on Image Processing, 2012, 21(3): 1339–1351
https://doi.org/10.1109/TIP.2011.2169269
|
5 |
Feng J, Jiao L, Zhang X, Sun T. Hyperspectral band selection based on trivariate mutual inforamtion and clonal selection. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(7): 4092–4105
https://doi.org/10.1109/TGRS.2013.2279591
|
6 |
Qian D, He Y. Similarity-based unsupervised band selection for hyperspectral image analysis. IEEE Geoscience and Remote Sensing Letters, 2008, 5(4): 564–568
https://doi.org/10.1109/LGRS.2008.2000619
|
7 |
Gustavo C, Joris M, Bernhard S. Remote sensing feature selection by kernel dependence measures. IEEE Geoscience and Remote Sensing Letters, 2010, 7(3): 587–591
https://doi.org/10.1109/LGRS.2010.2041896
|
8 |
He Y, Qian D, Su H, Sheng Y. An efficient method for supervised hyperspectral band selection. IEEE Geoscience and Remote Sensing Letters, 2011, 8(1): 138–142
https://doi.org/10.1109/LGRS.2010.2053516
|
9 |
Shen L, Zhu Z, Jia S, Zhu J, Sun Y. Discriminative gabor feature selection for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters, 2013, 10(1): 29–33
https://doi.org/10.1109/LGRS.2012.2191761
|
10 |
Qian Y, Ye M, Zhou J. Hyperspectral image classification based on structured sparse logistic regression and three-dimensional wavelet texure features. IEEE Transactions on Geoscience and Remote Sensing, 2012, 51(4): 2276–2291
https://doi.org/10.1109/TGRS.2012.2209657
|
11 |
Claude C, Kacem C, Steven L. BandClust: an unsupervised band reduction method for hyperspectral remote sensing. IEEE Geoscience and Remote Sening Letters, 2011, 8(3): 565–569
https://doi.org/10.1109/LGRS.2010.2091673
|
12 |
Chris B. Pattern Recognition and Machine Learning. Springer, 2006,325–345
|
13 |
Cai X, Wen G, Wei J, Yu Z. Relative manifold based semi-supervised dimensionality. Frontiers of Computer Science, 2014, 8(6): 923–932
https://doi.org/10.1007/s11704-014-3193-8
|
14 |
Li W, Guo Q, Charles E. A positive and unlabeled learning algorithm for one-class classification of remote-sensing data. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(2): 717–725
https://doi.org/10.1109/TGRS.2010.2058578
|
15 |
Priscilla R, Swamynathan S. A semi-supervised hierarchical approach: two dimensional clustering of microarray gene expression data. Frontiers of Computer Science, 2013, 7(2): 204–213
https://doi.org/10.1007/s11704-013-1076-z
|
16 |
Yang L, Yang S, Jin P, Zhang R. Semis-upervised hyperspectral image classification using spatio-spectral laplacian support vector machine. IEEE Geoscience and Remote Sening Letters, 2014, 11(3): 651–655
https://doi.org/10.1109/LGRS.2013.2273792
|
17 |
Wei D, Melba M. Active learning via multi-view and local proximity co-regulatization for hyperspectral image classification. IEEE Journal of Selected Topics in Signal Processing, 2011, 5(3): 618–628
https://doi.org/10.1109/JSTSP.2011.2123077
|
18 |
Swarnajyoti P, Lorenzo B. A fast cluster-assumption based activelearning technique for classification of remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(5): 1617–1626
https://doi.org/10.1109/TGRS.2010.2083673
|
19 |
Begum D, Claudio P, Lorenzo B. Batch-mode active-learning methods for the interactive classification of remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(3): 1014–1031
https://doi.org/10.1109/TGRS.2010.2072929
|
20 |
Gregg V, Robert O, Thomas G, Harry T, Earl G, Wallace M. The airboren visible/infrared imaging spectrometer. Remote Sensing of Enviroment, 1993, 44(3): 127–143
|
21 |
Joseph L, Nicewander W. Thirteen ways to look at the correlation coefficient. The American Statistician, 1988, 42(1): 59–66
|
22 |
Warner T, Steinmaus K, Foote B. An evaluation of spatial autocorrelation feature selection. International Jounral of Remote Sensing, 1999, 20(8): 1601–1606
https://doi.org/10.1080/014311699212632
|
23 |
Masashi S. Dimensionality reduction of multimodal labeled data by local fisher discriminant analysis. Journal of Machine Learning Research, 2007, 8(3): 1027–1061
|
24 |
Landgrebe D. Signal Theory Methods in Multispectral Remote Sensing. Hoboken: Wiley, 2003
https://doi.org/10.1002/0471723800
|
25 |
Yu X, Yang J, Xie Z. Training SVMs on a bound vectors set based on fisher projection. Frontiers of Computer Science, 2014, 8(5): 793–806
https://doi.org/10.1007/s11704-014-3161-3
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
|
Shared |
|
|
|
|
|
Discussed |
|
|
|
|