Modified Nonparametric Discriminant Analysis for Classification of Hyperspectrl Images With Limited Training Samples

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Kianisarkaleh Azadeh; ghasemian mohammad hasan

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Journal: SIGNAL AND DATA PROCESSING Year:2017 | Volume:14 | Issue:2 (serial 32) Page(s): 43-57

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Title

Modified Nonparametric Discriminant Analysis for Classification of Hyperspectrl Images With Limited Training Samples

Author(s)

Kianisarkaleh Azadeh | ghasemian mohammad hasan | Issue Writer Certificate

Keywords

Hyperspectral imagesQ2

Feature extractionQ2

Supervised classificationQ1

Hughes PhenomenonQ1

Limited training samplesQ1

Abstract

Feature extraction performs an important role in improving hyperspectral image classification. Compared with parametric methods, nonparametric Feature extraction methods have better performance when classes have nonnormal distribution. Besides, these methods can extract more features than parametric Feature extraction methods do. Nonparametric Feature extraction methods use nonparametric scatter matrices to compute transformation matrix. Nonparametric Discriminant Analysis (NDA) is one of the nonparametric Feature extraction methods in which, in order to form nonparametric scatter matrices, local means of samples and weight function are used. Local mean is calculated by k nearest neighbors of each sample and weight function emphasizes on boundary samples in between class scatter matrix formation. In this paper, modified NDA (MNDA) is proposed to improve NDA. In MNDA, the number of neighboring samples, when measuring local mean, are determined considering position of each sample in feature space. MNDA uses new weight functions in scatter matrix formation. Suggested weight functions emphasizes on boundary samples in between class scatter matrix formation and focus on samples close to class mean in within class scatter matrix formation. Moreover, within class scatter matrix is regularized to avoid singularity. Experimental results on Indian Pines and Salinas images show that MNDA has better performance compared to other parametric and nonparametric Feature extraction methods. For Indian Pines data set, the maximum average classification accuracy is 80. 34%, which is obtained by 18 training samples, support vector machine (SVM) classifier and 10 extracted features achieved by MNDA method. For Salinas data set, the maximum average classification accuracy is 94. 31%, which is obtained by 18 training samples, SVM classifier and 9 extracted features achieved by MNDA method. Experiments show that using suggested weight functions and regularized within class scatter matrix, the proposed method obtained better results in hyperspectrl imag classification with Limited training samples.

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APA: Copy

Kianisarkaleh, Azadeh, & ghasemian, mohammad hasan. (2017). Modified Nonparametric Discriminant Analysis for Classification of Hyperspectrl Images With Limited Training Samples. SIGNAL AND DATA PROCESSING, 14(2 (serial 32) ), 43-57. SID. https://sid.ir/paper/160753/en

Vancouver: Copy

Kianisarkaleh Azadeh, ghasemian mohammad hasan. Modified Nonparametric Discriminant Analysis for Classification of Hyperspectrl Images With Limited Training Samples. SIGNAL AND DATA PROCESSING[Internet]. 2017;14(2 (serial 32) ):43-57. Available from: https://sid.ir/paper/160753/en

IEEE: Copy

Azadeh Kianisarkaleh, and mohammad hasan ghasemian, “Modified Nonparametric Discriminant Analysis for Classification of Hyperspectrl Images With Limited Training Samples,” SIGNAL AND DATA PROCESSING, vol. 14, no. 2 (serial 32) , pp. 43–57, 2017, [Online]. Available: https://sid.ir/paper/160753/en

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