*Article* **Recursive Local Summation of RX Detection for Hyperspectral Image Using Sliding Windows**

**Liaoying Zhao 1 , Weijun Lin 1 , Yulei Wang 2,\* and Xiaorun Li 3**


Received: 29 November 2017; Accepted: 9 January 2018; Published: 13 January 2018

**Abstract:** Anomaly detection has received considerable interest for hyperspectral data exploitation due to its high spectral resolution. Fast processing and good detection performance are practically significant in real world problems. Aiming at these requirements, this paper develops a recursive local summation RX anomaly detection approach by virtue of sliding windows. This paper develops a recursive local summation RX anomaly detection approach by virtue of sliding windows. A causal sample covariance/correlation matrix is derived for local window background. As for the real-time sliding windows, the *Woodbury* identity is used in recursive update equations, which could avoid the calculation of historical information and thus speed up the processing. Furthermore, a background suppression algorithm is also proposed in this paper, which removes the current under test pixel from the recursively update processing. Experiments are implemented on a real hyperspectral image. The experiment results demonstrate that the proposed anomaly detector outperforms the traditional real-time local background detector and has a significant speed-up effect on calculation time compared with the traditional detectors.

**Keywords:** hyperspectral imagery; recursive anomaly detection; local summation RX detector (LS-RXD); sliding window
