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 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.
Highlights
Attributed to the high spectral resolution, hyperspectral images are capable of uncovering many subtle signal sources that cannot be known by prior knowledge or be visually inspected by image analysts [1,2]
To demonstrate the performance of anomaly detection using recursive local summation RXD, two real hyperspectral image scenes were conducted for experiments
This paper proposes a recursive local summation RX algorithm for hyperspectral anomaly detection based on sliding window processing
Summary
Attributed to the high spectral resolution, hyperspectral images are capable of uncovering many subtle signal sources that cannot be known by prior knowledge or be visually inspected by image analysts [1,2]. Several real-time anomaly detection methods [14,15,16,17,18,19] have been proposed. The computational performance of real-time causal linewise progressive anomaly detection (RCLPAD) based on Cholesky decomposition along with linear system solving were developed in [17]. An advanced anomaly detector using causal sliding array windows to capture local autocorrelation matrix statistics in the sense of causality was developed (CSA-RXD) [18], by virtue of causal sliding windows, a causal sample correlation matrix can be derived for causal anomaly detection. Compared with sliding array window, setting a sliding square window usually contains much more spectral-spatial integration information This paper addresses this issue and further develops the recursive processing for LS-RXD based on sliding square window.
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call