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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

resumen

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.

palabra clave

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

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