Investigating temporal distributions for spectral anomaly detection through time

Hope Simonoko; Amanda Ziemann; Eric Flynn

doi:10.1117/12.2557894

19 May 2020 Investigating temporal distributions for spectral anomaly detection through time

Hope Simonoko, Amanda Ziemann, Eric Flynn

Proceedings Volume 11392, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imagery XXVI; 113920X (2020) https://doi.org/10.1117/12.2557894
Event: SPIE Defense + Commercial Sensing, 2020, Online Only

Abstract

The Reed-Xiaoli (RX) detector is used for identifying spatial anomalies in multispectral imagery, which are pixels whose spectra are anomalous relative to other pixels in a scene. The distribution of the spectra in an image is used to represent the background, and the anomalies are the pixels whose spectra deviate statistically from this distribution. While RX is used to identify spatial anomalies, in this research we have instead developed a method to capture temporal anomalies, or fleeting changes, such as a music festival in the desert. Using the annual Burning Man festival as a test case, we use a time series of multispectral images and iterate through each pixel, drawing the "background" distribution from a particular pixel location over time. Temporal RX (TRX) thus compares a pixel against itself through time, which enables us to capture normal seasonal trends and identify fleeting changes. We also describe a local window variant called Local Temporal RX (LTRX). Using k-means clustering and a new approach deemed Meta-RX, we investigate the nature of the temporal anomalies detected by TRX and LTRX to infer types and causes of change.

Citation Download Citation

Hope Simonoko, Amanda Ziemann, and Eric Flynn "Investigating temporal distributions for spectral anomaly detection through time", Proc. SPIE 11392, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imagery XXVI, 113920X (19 May 2020); https://doi.org/10.1117/12.2557894

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