Evaluating subspace clustering techniques for effective and efficient dimensionality reduction in hyperspectral imaging

Hyperspectral image processing (HSI) is a critical task in remote sensing, medical imaging, and various other fields due to its capacity to capture detailed spectral information across numerous bands. However, the high dimensionality of hyperspectral data often leads to increased computational burden and complexity. This study investigates the use of subspace clustering techniques for dimensionality reduction in hyperspectral images, focusing on methods such as sparse subspace clustering (SSC), low-rank representation (LRR), and spectral clustering. These techniques are evaluated for their ability to preserve both spectral and spatial features while reducing data dimensionality. Through detailed comparison, the study finds that SSC offers superior performance in terms of classification accuracy and computational efficiency, particularly in handling the intricate patterns in high-dimensional hyperspectral image datasets. The insights gained from this analysis contribute to a better understanding of the strengths and limitations of different subspace clustering methods, providing valuable guidance for future advancements in image processing and hyperspectral data analysis.