Anomaly Detection (AD) has recently become an important application of target detection in hyperspectral images. The Reed-Xialoi (RX) is the most widely used AD algorithm that suffers from “small sample size” problem. The best solution for this problem is to use Dimensionality Reduction (DR) techniques as a pre-processing step for RX detector. Using this method not only improves the detection p...

anomaly detection (ad) has recently become an important application of target detection in hyperspectral images. the reed-xialoi (rx) is the most widely used ad algorithm that suffers from “small sample size” problem. the best solution for this problem is to use dimensionality reduction (dr) techniques as a pre-processing step for rx detector. using this method not only improves the detection p...

Due to abundant spectral information contained in the hyperspectral images, they are suitable data for anomalous targets detection. The use of spatial features in addition to spectral ones can improve the anomaly detection performance. An anomaly detector, called nonparametric spectral-spatial detector (NSSD), is proposed in this work which utilizes the benefits of spatial features and local st...

The RX anomaly detector is well known for its unsupervised ability to detect anomalies in hyperspectral images (HSI). However, the RX method assumes the data is uncorrelated and homogeneous, both of which are not inherent in HSI data. To defeat the correlation and homogeneity, a new method dubbed Iterative Linear RX is proposed. Rather than the test pixel being inside a window used by RX, Itera...

This research involves simulating remote sensing conditions using previously collected hyperspectral imagery (HSI) data. The Reed–Xiaoli (RX) anomaly detector is well-known for its unsupervised ability to detect anomalies in hyperspectral images. However, the RX detector assumes uncorrelated and homogeneous data, both of which are not inherent in HSI data. To address this difficulty, we propose...

Anomaly detection in hyperspectral data has received a lot of attention for various applications and is especially important for defence and security. The aim of anomaly detection is to detect pixels in the hyperspectral data cube whose spectra differ significantly from the background spectra. Many types of anomaly detectors have been proposed in literature. They differ by the way the backgroun...

We present an unsupervised anomaly detection method for hyperspectral imagery (HSI) based on data characteristics inherit in HSI. A locally adaptive technique of iteratively refining the well-known RX detector (LAIRX) is developed. The technique is motivated by the need for better firstand second-order statistic estimation via avoidance of anomaly presence. Overall, experiments show favorable R...

Real-time anomaly detection has received wide attention in remote sensing image processing because many moving targets must be detected on a timely basis. A widely-used anomaly detection algorithm is the Reed-Xiaoli (RX) algorithm that was proposed by Reed and Yu. The kernel RX algorithm proposed by Kwon and Nasrabadi is a nonlinear version of the RX algorithm and outperforms the RX algorithm i...

Anomaly detection is attractive for the analysis of hyperpectral imagery. This paper describes an expanded anomaly detection algorithm for small targets in hyperspectral imagery. As a variant of the well known multivariate anomaly detector called RX algorithm, the approach called the dimension reduction RX algorithm (DRRX) is proposed. The analytical fusion strategy is incorporated into the RX ...