Abstract
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.
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Acknowledgement
This work is supported by the China Scholarship Council, the National Science Foundation Grant 0705836, and the Laboratory for Applications of Remote Sensing at Purdue University.
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Li Ma is a Ph.D. candidate at Huazhong University of Science and Technology, and currently a visiting scholar at Purdue University.
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Ma, L., Crawford, M.M. & Tian, J. Anomaly Detection for Hyperspectral Images Based on Robust Locally Linear Embedding. J Infrared Milli Terahz Waves 31, 753–762 (2010). https://doi.org/10.1007/s10762-010-9630-3
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DOI: https://doi.org/10.1007/s10762-010-9630-3