Machine Learning for Detecting Anomalies in SAR Data

One of most common algorithms for anomaly detection in multi-dimensional imagery is the Reed - Xiaoli (RX) algorithm; it gives each pixel a score that defines its likelihood to be an anomaly. We have implemented a new algorithm which uses both RX and the Non-Negative Matrix Factorization (NNMF) learning algorithm in order to pick an adaptive threshold for detection; we have applied it to Synthetic Aperture Radar (SAR) data. The NNMF approach is defined as a minimization problem which approximates the given data by extracting its main trends. By comparing the original data to the reduced data, we can divide the image anomalies into two different groups, where one group contains the anomalies which are part of the image main trends and the second group contains the anomalies of the sub trends. With this division, we can pick an adaptive threshold for each of the groups according to its unique characteristics.