Anomaly Detection for Hyperspectral Images Based on Robust Locally Linear Embedding

作者：Ma Li^*; Crawford Melba M; Tian Jinwen

来源：Journal of Infrared Millimeter and Terahertz Waves, 2010, 31(6): 753-762.

DOI：10.1007/s10762-010-9630-3

• Hyperspectral images
• Anomaly detection
• Robust locally linear embedding (RLLE)
• Dimensionality reduction (DR)
• RX detector

In this paper, anomaly detection in hyperspectral images is investigated using robust locally linear embedding (RLLE) for dimensionality reduction in conjunction with the RX anomaly detector. The new RX-RLLE method is implemented for large images by subdividing the original image and applying the RX-RLLE operations to each subset. Moreover, from the kernel view of LLE, it is demonstrated that the RX-RLLE is equivalent to introducing a locally linear embedding (LLE) kernel into the kernel RX (KRX) algorithm. Experimental results indicate that the RX-RLLE has good anomaly detection performance and that RLLE has superior performance to LLE and principal component analysis (PCA) for dimensionality reduction in the application of anomaly detection.

• 出版日期2010-6
• 单位华中科技大学