سابِق|تال

Feature extraction of hyperspectral remote sensing image based on optimized Discriminative Locality Alignment

  • role： First author第一作者
  • Affiliation：

    School of Earth Science and Engineering, Hohai University, Nanjing 211100, China

  • Email：hjsu@hhu.edu.cn
  • Introduction：苏红军，1985年生，男，教授，研究方向为高光谱遥感、资源环境遥感等。E-mail: hjsu@hhu.edu.cn
SU Hongjun,  
  • Affiliation：

    School of Earth Science and Engineering, Hohai University, Nanjing 211100, China

GU Mengyu

ملخص

Discriminative Locality Alignment (DLA) is a linear feature extraction algorithm, which has exhibited effectiveness in many fields. The algorithm can deal with the nonlinearity of samples distribution, preserve discriminative information over local patches, and avoid the problem of matrix singularity in calculation. Generally, the PCA step is recommended for reducing noise. However, PCA extracts noise images with low order components, and bands with a small variance do not necessarily indicate poor image quality. To effectively reduce the influence of noise on DLA and further improve the accuracy of DLA in the feature extraction of hyperspectral remote sensing images, a linear feature extraction algorithm of MDLA and a nonlinear feature extraction algorithm of KMDLA are proposed in this paper. The key idea of MDLA is as follows. First, MNF transforms data from the original space into a new subspace. In the process, the SNR is used to improve the order of components, and the noise in the image can be effectively reduced by sorting according to the image quality. Second, DLA is performed in the new subspace to obtain the result of feature extraction. KMDLA is the kernel method of MDLA. The main idea of KMNF is as follows. First, the samples are mapped to the high-dimensional feature space by the kernel function in which MNF is conducted. Finally, DLA is performed in the subspace spanned by KMNF, further enhancing the nonlinear discriminant ability of MDLA for the samples. Three sets of hyperspectral remote sensing images were selected as the study area. A detailed comparison of the proposed algorithms and five algorithms, namely, the PCA, MNF, LDA, CCPGE, and DLA algorithms, is provided in this paper. For a more intuitive comparison, experiments were conducted to classify the images of all bands. The support vector machine classifier was used to classify the results of feature extraction. Experimental results demonstrate that both MDLA and KMDLA outperform the five algorithms of comparison. Compared with DLA, the proposed algorithms exhibited respective improvements of 2.06% and 2.74% on the Purdue Campus data, 1.91% and 2.45% on the Indian Pines data, and 1.41% and 2.04% on the Salinas data. In terms of efficiency , MDLA and KMDLA consumed less operation time for classification than DLA when the experimental data was small. KMDLA consumed the most operation time when the experimental data was large, whereas MDLA always consumed minimal operation time. The effect of image quality on the performance of different dimensionality reduction methods is discussed based on the Purdue Campus data. Results show that MDLA and KMDLA can still obtain good classification results with the increasing noise in the image. Therefore, the two proposed algorithms can effectively reduce the noise of hyperspectral remote sensing images, improve the classification accuracy, and classify hyperspectral remote sensing images accurately.

مفهوم

hyperspectral remote sensing;feature extraction;MDLA;KMDLA;image classification

References

  1. 1.
    Bach F R and Jordan M I. 2003. Kernel independent component analysis. Journal of Machine Learning Research, 3(1): 1-48
  2. 2.
    Bachmann C M, Ainsworth T L and Fusina R A. 2005. Exploiting manifold geometry in hyperspectral imagery. IEEE Transactions on Geoscience and Remote Sensing, 43(3): 441-454
  3. 3.
    Baudat G and Anouar F. 2000. Generalized discriminant analysis using a kernel approach. Neural Computation, 12(10): 2385-2404
  4. 4.
    Belkin M and Niyogi P. 2003. Laplacian eigenmaps for dimensionality reduction and data representation. Neural Computation, 15(6): 1373-1396
  5. 5.
    Bengio Y, Paiement J F, Vincent P, Delalleau O, Roux N L and Ouimet M. 2004. Out-of-sample extensions for LLE, isomap, MDS, eigenmaps, and spectral clustering//Thrun S, Saul L K and Schölkopf B, eds. Advances in Neural Information Processing Systems. Cambridge: MIT Press: 177-184
  6. 6.
    Du P J, Xia J S, Xue Z H, Tan K, Su H J and Bao R. 2016. Review of hyperspectral remote sensing image classification. Journal of Remote Sensing, 20(2): 236-256
  7. 7.
    Farrell M D and Mersereau R M. 2005. On the impact of PCA dimension reduction for hyperspectral detection of difficult targets. IEEE Geoscience and Remote Sensing Letters, 2(2): 192-195
  8. 8.
    Gao L R, Zhao B, Jia X P, Liao W Z and Zhang B. 2017. Optimized kernel minimum noise fraction transformation for hyperspectral image classification. Remote Sensing, 9(6): 548
  9. 9.
    Green A A, Berman M, Switzer P and Craig M D. 1988. A transformation for ordering multispectral data in terms of image quality with implications for noise removal. IEEE Transactions on Geoscience and Remote Sensing, 26(1): 65-74
  10. 10.
    Guo Y C, Ding G G, Liu L, Han J G and Shao L. 2017. Learning to hash with optimized anchor embedding for scalable retrieval. IEEE Transactions on Image Processing, 26(3): 1344-1354
  11. 11.
    Jia Y F, Tian Y, Li Y J and Fu P B. 2017. Exponential discriminative locality alignment for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters, 14(1): 33-37
  12. 12.
    Lin N, Yang W N and Wang B. 2014. Multi-class SVM classification for hyperspectral remote sensing image based on kernel minimum noise fraction transform. Computer Applications and Software, 31(6): 116-119
  13. 13.
    Liu N, Li W and Du Q. 2018. Unsupervised feature extraction for hyperspectral imagery using collaboration-competition graph. IEEE Journal of Selected Topics in Signal Processing, 12(6): 1491-1503
  14. 14.
    Liu Z H and Jin Z. 2011. Enhanced discriminative locality alignment and its kernel extension. Optical Engineering, 50(8): 087002
  15. 15.
    Luo G C, Chen G Y, Tian L, Qin K and Qian S E. 2016. Minimum noise fraction versus principal component analysis as a preprocessing step for hyperspectral imagery denoising. Canadian Journal of Remote Sensing, 42(2): 106-116
  16. 16.
    Luo R B, Wen Y, Liu X N and Wu Z F. 2018. Feature extraction of hyperspectral images with semi-supervised Sparse graph learning//Proceedings of 2018 Fifth International Workshop on Earth Observation and Remote Sensing Applications (EORSA). Xi’an: IEEE
  17. 17.
    Nielsen A A. 2011. Kernel maximum autocorrelation factor and minimum noise fraction transformations. IEEE Transactions on Image Processing, 20(3): 612-624
  18. 18.
    Roweis S T and Saul L K. 2000. Nonlinear dimensionality reduction by locally linear embedding. Science, 290(5500): 2323-2326
  19. 19.
    Schölkopf B, Smola A and Müller K R. 1997. Kernel principal component analysis//Proceedings of the 7th International Conference on Artificial Neural Networks. Switzerland: Springer: 583-588
  20. 20.
    Shawe-Taylor J and Cristianini N. 2004. Kernel Methods for Pattern Analysis. Cambridge: Cambridge University Press: 115-116
  21. 21.
    Sun W W and Du Q. 2019. Hyperspectral band selection: a review. IEEE Geoscience and Remote Sensing Magazine, 7(2): 118-139
  22. 22.
    Sun W W, Yang G, Du B, Zhang L F and Zhang L P. 2017. A sparse and low-rank near-isometric linear embedding method for feature extraction in hyperspectral imagery classification. IEEE Transactions on Geoscience and Remote Sensing, 55(7): 4032-4046
  23. 23.
    Tao D P, Liang L Y, Jin L W and Gao Y. 2014. Similar handwritten Chinese character recognition by kernel discriminative locality alignment. Pattern Recognition Letters, 35: 186-194
  24. 24.
    Tong Q X, Zhang B and Zheng L F. 2006. Hyperspectral Remote Sensing: the Principle, Technology and Application. Beijing: Higher Education Press
  25. 25.
    Wang M Y, Wan Y C, Ye Z W, Gao X J and Lai X D. 2018. A band selection method for airborne hyperspectral image based on chaotic binary coded gravitational search algorithm. Neurocomputing, 273: 57-67
  26. 26.
    Zhan Y, Hu D, Xing H H and Yu X C. 2017. Hyperspectral band selection based on deep convolutional neural network and distance density. IEEE Geoscience and Remote Sensing Letters, 14(12): 2365-2369
  27. 27.
    Zhang B. 2016. Advancement of hyperspectral image processing and information extraction. Journal of Remote Sensing, 20(5): 1062-1090
  28. 28.
    Zhang L F, Zhang L P, Tao D C, Huang X and Xia G S. 2013. Nonnegative discriminative manifold learning for hyperspectral data dimension reduction//Proceedings of 2013 5th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS). Gainesville: IEEE: 1-4
  29. 29.
    Zhang M Y, Ma J J and Gong M G. 2017. Unsupervised hyperspectral band selection by fuzzy clustering with particle swarm optimization. IEEE Geoscience and Remote Sensing Letters, 14(5): 773-777
  30. 30.
    Zhang T H, Tao D C, Li X L and Yang J. 2009. Patch alignment for dimensionality reduction. IEEE Transactions on Knowledge and Data Engineering, 21(9): 1299-1313
  31. 31.
    Zhang Z Y and Zha H Y. 2004. Principal manifolds and nonlinear dimensionality reduction via tangent space alignment. Journal of Shanghai University (English Edition), 8(4): 406-424

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