Integrating Fractal Dimension and Time Series Analysis for Optimized Hyperspectral Image Classification

Hyperspectral images contain a huge number of spectral channels, which ensures high accuracy of analysis, but at the same time leads to problems associated with data redundancy, high computational load and decreased classification efficiency. In this paper, a method is proposed to integrate time series complexity analysis and fractal dimension (FD) to effectively reduce the dimensionality of hyperspectral data. Each pixel is considered as a time series characterized by spectral complexity, which allows identifying the most informative parts of the spectrum. Fractal dimension is used to quantify the complexity of spectral features and select significant channels. The proposed approach allows preserving critical information while minimizing losses during dimensionality reduction. To evaluate the effectiveness of the method, a comparison of the classification accuracy using the support vector machine (SVM) algorithm was carried out before and after applying the proposed optimization procedure. Experimental results on real hyperspectral data show significant dimensionality reduction while maintaining or improving classification quality.