Fractal-Based Ensemble Classification System for Hyperspectral Images

Abstract

According to the literature, the utilization of spatial features can significantly enhance the accuracy of hyperspectral image (HSI) classification. Fractal features are powerful measures of texture, representing the local complexity of an image. In HSI classification, textural features are typically extracted from dimensionally reduced data cubes, such as principal component analysis (PCA). However, the effectiveness of textures obtained from alternative feature extraction (FE) methods in improving classification accuracy has not been extensively investigated. This study introduces a new ensemble support vector machine classification system that combines spectral features derived from PCA, minimum noise fraction (MNF), linear discriminant analysis (LDA), and fractal features derived from these FE methods. The final results on two HSI datasets, namely, Indian Pines (IP) and Pavia University (PU), demonstrate that the proposed classification method achieves approximately 95.75% and 99.36% accuracies, outperforming several other spatial–spectral HSI classification methods.