Feature Extraction for Hyperspectral Remote Sensing Image Based on Local Fisher Discriminant Analysis with Wavelet Kernel

被引：0
|
作者：
Zhang H. [1 ,2 ]
Liu W. [2 ]
Lü H. [2 ]
机构：
[1] School of Electronic and Information Engineering, Liaoning Technical University, Huludao
[2] School of Software, Liaoning Technique University, Huludao
来源：
Moshi Shibie yu Rengong Zhineng/Pattern Recognition and Artificial Intelligence | 2019年 / 32卷 / 07期
基金：
中国国家自然科学基金;
关键词：
Feature Extraction; Hyperspectral Image Classification; Local Fisher Discriminant Analysis; Wavelet Kernel Function;
D O I：
10.16451/j.cnki.issn1003-6059.201907006
中图分类号：
学科分类号：
摘要：
To improve classification accuracies of hyperspectral remote sensing images and make full use of local information, a feature extraction method for hyperspectral remote sensing images based on local Fisher discriminant analysis with wavelet kernel is proposed. Wavelet kernel function is introduced to map data from a low dimensional space to a high dimensional feature space, and a weighted matrix is employed to calculate scatter matrices. Local Fisher discriminant criterion function is solved to obtain the optimal feature matrix and a better separation in high-dimensional feature space. Experimental results on two open hyperspectral datasets show that the overall classification accuracy and Kappa coefficient of the proposed method are improved compared with other methods. © 2019, Science Press. All right reserved.
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页码：624 / 632
页数：8
相关论文
共 24 条
  • [1] Su H.J., Zhao B., Du Q., Et al., Kernel Equation Collaborative Representation with Local Correlation Features for Hyperspectral Image Classification, IEEE Transactions on Geoscience and Remote Sensing, (2018)
  • [2] Li L., Sun C., Lin L.L., Et al., A Dual-Kernel Spectral-Spatial Cla-ssification Approach for Hyperspectral Images Based on Mahalanobis Distance Metric Learning, Information Sciences, 429, pp. 260-283, (2018)
  • [3] Imani M., Manifold Structure Preservative for Hyperspectral Target Detection, Advances in Space Research, 61, 10, pp. 2510-2520, (2018)
  • [4] Du P.J., Xia J.S., Xue Z.H., Et al., Review of Hyperspectral Remote Sensing Image Classification, Journal of Remote Sensing, 20, 2, pp. 236-256, (2016)
  • [5] Corson M.R., Lucke R.L., Davis C.O., Et al., The Hyperspectral Imager for the Coastal Ocean (HICO<sup>TM</sup>) Environmental Littoral Imaging from the International Space Station, Proc of the IEEE International Geoscience and Remote Sensing Symposium, pp. 3752-3755, (2010)
  • [6] Li N., Huang X.C., Zhao H.J., Et al., Multiparameter Optimization for Mineral Mapping Using Hyperspectral Imagery, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11, 4, pp. 1348-1357, (2018)
  • [7] Liu Y.F., Ye D.Y., Unsupervised Feature Selection Algorithm Based on Neighborhood Preserving Learning, Pattern Recognition and Artificial Intelligence, 31, 12, pp. 1096-1102, (2018)
  • [8] Chen G.Y., Qian S.E., Denoising of Hyperspectral Imagery Using Principal Component Analysis and Wavelet Shrinkage, IEEE Transactions on Geoscience and Remote Sensing, 49, 3, pp. 973-980, (2011)
  • [9] Moncayo S., Duponchel L., Mousavipak N., Et al., Exploration of Megapixel Hyperspectral LIBS Images Using Principal Component Analysis, Journal of Analytical Atomic Spectrometry, 33, 2, pp. 210-220, (2018)
  • [10] Bandos T.V., Bruzzone L., Camps-Valls G., Classification of Hyperspectral Images with Regularized Linear Discriminant Analysis, IEEE Transactions on Geoscience and Remote Sensing, 47, 3, pp. 862-873, (2009)
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