Ensemble Learning Based Multiple Kernel Principal Component Analysis for Dimensionality Reduction and Classification of Hyperspectral Imagery

Abstract

Classification is one of the most challenging tasks of remotely sensed data processing, particularly for hyperspectral imaging (HSI). Dimension reduction is widely applied as a preprocessing step for classification; however the reduction of dimension using conventional methods may not always guarantee high classification rate. Principal component analysis (PCA) and its nonlinear version kernel PCA (KPCA) are known as traditional dimension reduction algorithms. In a previous work, a variant of KPCA, denoted as Adaptive KPCA (A-KPCA), is suggested to get robust unsupervised feature representation for HSI. The specified technique employs several KPCAs simultaneously to obtain better feature points from each applied KPCA which includes different candidate kernels. Nevertheless, A-KPCA neglects the influence of subkernels employing an unweighted combination. Furthermore, if there is at least one weak kernel in the set of kernels, the classification performance may be reduced significantly. To address these problems, in this paper we propose an Ensemble Learning (EL) based multiple kernel PCA (M-KPCA) strategy. M-KPCA constructs a weighted combination of kernels with high discriminative ability from a predetermined set of base kernels and then extracts features in an unsupervised fashion. The experiments on two different AVIRIS hyperspectral data sets show that the proposed algorithm can achieve a satisfactory feature extraction performance on real data.

[Retracted] A 3D‐2D Convolutional Neural Network and Transfer Learning for Hyperspectral Image Classification

“…To address the abovementioned challenges, dimensionality reduction (DR) [ 9 – 12 ] and semisupervised classification [ 13 , 14 ] approaches have been extensively adopted for HSIs. Generally, there are two classes of DR, i.e., the band selection and feature extraction [ 15 ].…”

Partitioned Relief-F Method for Dimensionality Reduction of Hyperspectral Images

“…Li X et al proposed a Two-stage Subspace Projection framework, in which they used the KPCA method to carry out feature projection of HSI data [25]. Further improvement methods have been proposed on the basis of KPCA, such as Super pixelwise KPCA [26] and Multiple KPCA based on integrated learning [27].…”

Novel approach for automatic mid‐diastole frame detection in 2D echocardiography sequences for performing planimetry of the mitral valve orifice

“…The second step is the construction of the covariance matrix of the data, and in the next stage, eigenvectors and eigenvalues of the covariance matrix are calculated by the singular value decomposition (SVD) as follows: The is the covariance matrix, and are eigenvectors and eigenvalues of the covariance matrix, respectively. Finally, for data mapping to 2D space, eigenvectors corresponding to the two largest eigenvalues are selected, and by multiplying by the desired eigenvectors, mapping of data into 2D space is performed. The kernel PCA algorithm : The kernel PCA is the generalisation of the linear PCA that, unlike the standard linear PCA, allows nonlinear dimensionality reduction, which is helpful for complex data that cannot be well represented in a linear space [28]. In fact, in the kernel PCA algorithm, the data is projected into the new space by a non‐linear kernel function. Assume we have a non‐linear transformation which transfers data from D‐dimensional feature space to a high‐dimensional space being linearly separable in this space.…”