Empirical mode decomposition based decision fusion for higher hyperspectral image classification accuracy

Abstract

This paper proposes a novel Empirical Mode Decomposition (EMD) based decision fusion approach for accurate classification of hyperspectral images. The proposed method consists of three steps. In the first step, EMD, which iteratively decomposes the data into so called Intrinsic Mode Functions (IMFs) in accordance with the intrinsic characteristics of data, is applied to each hyperspectral image band for decomposition. In the second step, the IMFs are assumed as different representations of data, and original hyperspectral data as well as IMF based representations are classified by Support Vector Machine (SVM), independently from each other, to obtain independent decisions. In the final step, these independent decisions are fused by a decision fusion rule to get the final classification result. Provided experimental results demonstrate that the proposed EMD based decision approach results in improved SVM classification.Index Terms-Hyperspectral imaging, Empirical mode decomposition, Decision fusion, Support vector machine INTRODUCTIONObtaining an accurate classification map is an important research topic in the area of hyperspectral imaging in remote sensing applications [1]. Decision fusion techniques are indeed very promising methods in the context of hyperspectral image classification and can significantly improve the classification performance by fusing classification results of individual data sources after each data source has been preliminary classified. Such decision fusion approaches have been proposed to classify hyperspectral data in [2][3][4][5]. Hyperspectral data has been initially decomposed into several sources by taking into account the correlation of spectral bands in [2]. Then, each source is separately classified by Support Vector Machine (SVM) classifiers and the final decision is achieved by considering all outputs as inputs of an additional SVM classifier. The original hyperspectral data and its Extended Morphological Profiles (EMPs) (which are constructed by applying opening and closing operations to the first several principle components obtained by the principle component analysis) are separately classified by SVM classifiers to acquire independent decisions, which are finally fused to get a final classification result in [3]. The advantage of SVM for class separation and the ability of K-means for reducing the impact of spectral variation in homogeneous regions has been analyzed using a decision fusion approach in [4].This paper introduces a novel Empirical Mode Decomposition (EMD) based decision fusion approach for hyperspectral image classification. EMD, proposed in [5], deals with nonlinear and nonstationary data by decomposing signals into Intrinsic Mode Functions (IMFs). EMD has been recently given an increasing interest in the remote sensing community. Narrow-Band Interference for Synthetic Aperture Radar (SAR) data has been suppressed by the utilization of EMD in [6] and the number of bands contained in hyperspectral images has been reduced by exploiting EMD in [7]. Approaches ma...

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“…The most recent ways involving classification using hyperspectral data include extreme learning machine (ELM) [7–9], partial least squares discriminant analysis (PLS-DA) [10,11], and various deep learning algorithms [12,13]. Several other studies have focused on further improving the classification accuracy [14–16].…”