A subspace kernel learning method for feature extraction of the hyperspectral image

被引：0

作者：

Liu, Zhenlin ^{[1
]}

Gu, Yanfeng ^{[1
]}

Zhang, Ye ^{[1
]}

机构：

[1] School of Electronics and Information Engineering, Harbin Institute of Technology

来源：

Harbin Gongcheng Daxue Xuebao/Journal of Harbin Engineering University | 2014年 / 35卷 / 02期

关键词：

Data dimension reduction; Feature extraction; Hyperspectral images; Image classification; Kernel methods;

D O I：

10.3969/j.issn.1006-7043.201309025

中图分类号：

学科分类号：

摘要：

Feature extraction is quite valuable for the mining and utilization of valid information in hyperspectral remote-sensing imaging and the increase of subsequent classified applications. For improving the dimension reduction effect, a subspace-modulated kernel principal component analysis (SM-KPCA) method is proposed. With this method, the grouping natures of hyperspectral data are integrated into a uniform kernel method framework and a subspace-modulated kernel is constructed. SMK (subspace-modulated kernel) achieves a sparse modulation on the spectral waveband by means of feature grouping; in addition, it is a data-adaptive kernel for measuring the nonlinear similarities among the hyperspectral data specimens. With the proposed method, AVIRIS (airborne visible infrared imaging spectrometer) real hyperspectral imaging is applied for evaluation. Additionally, this method is compared with the conventional kernel method and the spectrally weighted kernel method. The experimental results show that the SM-KPCA method more sufficiently utilizes the complex and relevant physical characteristics between wavebands. Therefore, it outperforms both the conventional kernel methods and the spectrally weighted kernel method regarding the aspect of the classification of hyperspectral images.

引用

页码：238 / 244

页数：6

共 14 条

[1]

Vane G., Green R.O., Chrien T.G., Et al., The airborne visible/infrared imaging spectrometer (AVIRIS), Remote Sens Environ, 44, pp. 127-143, (1993)

[2]

Landgrebe D., Hyperspectral image data analysis as a high dimensional signal processing problem, IEEE Signal Process Mag, 19, 1, pp. 17-28, (2002)

[3]

Lin N., Yang W., Wang B., Hyperspectral image feature extraction via kernel minimum noise fraction transform, Geomatics and Information Science of Wuhan University, 38, 8, pp. 988-992, (2013)

[4]

Du P., Wang X., Tan K., Et al., Dimensionality reduction and feature extraction from hyperspectral remote sensing imagery based on manifold learning, Geomatics and Information Science of Wuhan University, 36, 2, pp. 148-152, (2011)

[5]

Jia X., Richards J.A., Segmented principal components transformation for efficient hyperspectral remote-sensing Image display and classification, IEEE Trans Geosci Remote Sens, 37, 1, pp. 538-542, (1999)

[6]

Camps-Valls G., Gomez-Chova L., Munoz-Mari J., Et al., Composite kernels for hyperspectral image classification, IEEE Geosci Remote Sens Lett, 3, 1, pp. 93-97, (2006)

[7]

Gu Y., Liu Y., Zhang Y., A soft classification algorithm based on spectral-spatial kernels in hyperspectral images, Second International Conference on Innovative Computing, Information and Control, ICICIC 2007, (2007)

[8]

Fauvele M., Chanussot J., Benediktsson J.A., Adaptive pixel neighborhood definition for the classification of hyperspectral images with support vector machines and composite kernel, IEEE International Conference on Image Processing, ICIP'08, pp. 1884-1887, (2008)

[9]

Tuia D., Ratle F., Pozdnoukhov A., Et al., Multisource composite kernels for urban-image classification, IEEE Geosci Remote Sens Lett, 7, 1, pp. 88-92, (2010)

[10]

Camps-Valls G., Marsheva T.V.B., Zhou D., Semi-supervised graph-based hyperspectral image classification, IEEE Trans Geosci Remote Sens, 45, 10, pp. 3044-3054, (2007)

← 1 2 →