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

• Published in: Remote sensing (Basel, Switzerland) Vol. 10; no. 1; p. 103
• Main Authors: Zhao, Liaoying, Lin, Weijun, Wang, Yulei, Li, Xiaorun
• Format: Journal Article
• Language: English
• Published: MDPI AG 01.01.2018
• Subjects: hyperspectral imagery; local summation RX detector (LS-RXD); recursive anomaly detection; sliding window
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs10010103

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 W o o d b u r y 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.