Median-spectral-spatial transformation of hyperspectral data for sub-pixel anomaly detection

This paper extends the field of hyperspectral anomaly and target detection by introducing a new approach for preprocessing hyperspectral image data. In this study, we investigate the Median-Spectral-Spatial Transformation as an approach to draw out the sub-pixel difference characterizations of anomalous spectra. By implementing this preprocessing step, we have realized a significant improvement in false alarm reduction with increased probability of detection for sub-pixel targets. Sub-pixel anomalies contain target information consisting of only a small fraction of an image pixel's surface reflected material content. To demonstrate the efficacy of our approach, we compare results from RX anomaly detection across multiple HSI images.