Single Growth Algorithm (SGA) Implementation with MATLAB for Endmember Extraction

This article provides a comprehensive guide to implementing the Single Growth Algorithm (SGA) using MATLAB, specifically designed for extracting 10 endmembers. SGA is a powerful algorithm with broad applications in image processing, data mining, and machine learning domains. Mastering its implementation offers significant advantages for multidimensional data analysis. The following sections detail the theoretical foundation of SGA and its practical MATLAB implementation. We will examine the algorithm's core mechanics, including its iterative growth process and convergence criteria. Key MATLAB functions such as spectral angle mapper calculations and dimensionality reduction techniques will be explained in context. After establishing the algorithmic framework, we demonstrate the complete workflow for extracting 10 distinct endmembers. This includes preprocessing steps like noise reduction, spectral normalization, and feature space optimization. The implementation incorporates cluster validation metrics and abundance estimation methods to ensure accurate endmember separation. The code architecture employs matrix operations for efficient spectral processing, utilizing MATLAB's built-in functions for linear algebra computations and statistical analysis. Particular attention is given to memory optimization strategies when handling large hyperspectral datasets. Through this tutorial, you will gain practical skills in implementing SGA for data processing and analysis, establishing a solid foundation for advanced research applications in spectral unmixing and pattern recognition.