Endmember Extraction for Hyperspectral Image Target Detection

For endmember extraction in hyperspectral image target detection, we can implement a multi-band data-based approach to enhance algorithm performance. Specifically, we can utilize multidimensional scaling (MDS) techniques for data preprocessing to obtain improved feature representations. The implementation typically involves calculating dissimilarity matrices between spectral vectors and reducing dimensionality while preserving significant spectral characteristics. Additionally, we can explore alternative feature extraction methods such as wavelet transform-based approaches, which involve decomposing spectral signals across different frequency bands using discrete wavelet transform (DWT) functions, or local binary pattern (LBP) methods that capture local texture features by comparing pixel values with their neighbors. These approaches enable comprehensive and accurate feature representations through algorithmic implementations that may include spectral angle mapper (SAM) calculations, linear spectral unmixing, or orthogonal subspace projection techniques. Through these explorations, we achieve more robust feature representations that significantly improve endmember extraction effectiveness for target detection applications.