Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution

Reed, I. S.; Yu, Xiaoli

doi:10.1109/29.60107

Cited by 1,504 publications

(817 citation statements)

References 6 publications

Supporting

Mentioning

726

Contrasting

Unclassified

Order By: Relevance

“…Another method of spatial subsetting, which is aimed at detecting the signal interfering with subpixel targets as well as increasing MVN of the background, is the RX algorithm [3]. The original algorithm implemented a combination of spatial and spectral matched filters, defining the spectral matched filter background with a sliding window to recalculate local background statistics for each pixel.…”

Section: Spatial Subsettingmentioning

confidence: 99%

“…The third moment (approaching zero) was used to approximate the normality of the spatial subset. Minimizing skew in this way was assumed to "create a distribution which is as close to Gaussian as possible" [3].…”

Section: Spatial Subsettingmentioning

confidence: 99%

“…Another and perhaps more fruitful approach to improved detection results is to improve the model of background, thus increasing the suppression of unwanted signal and creating greater separation between target detects and false alarms before scaling. Based on this concept, several methods for calculating background statistics have been developed in the field which are reported to improve detection results for specific targets, detectors, and HSI data sets [1] [2] [3]. Each of these methods takes cues from the imagery to establish a rationale for selecting which data is to be included in the estimation of background statistics.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Matched Filter Stochastic Background Characterization for Hyperspectral Target Detection

West¹,

Messinger²,

Ientilucci³

et al. 2005

View full text Add to dashboard Cite

Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. CAPT WEST JASON E FUNDING NUMBERS PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)ROCHESTER INSTITUTE OF TECHNOLOGY PERFORMING ORGANIZATION REPORT NUMBERCI04-1012 SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES) ABSTRACTAlgorithms exploiting hyperspectral imagery for target detection have continually evolved to provide improved detection results. Adaptive matched filters can be used to locate spectral targets by modeling scene background as either structured (geometric) with a set of endmembers (basis vectors) or as unstructured (stochastic) with a covariance or correlation matrix. These matrices are often calculated using all available pixels in a data set. In unstructured background research, various techniques for improving over the scene-wide method have been developed, each involving either the removal of target signatures from the background model or the segmenting of image data into spatial or spectral subsets. Each of these methods increase the detection signal to background ratio (SBR) and the multivariäte normality (MVN) of the data from which background statistics are calculated, thus increasing separation between target and non-target species in the matched filter detection statistic and ultimately improving thresholded target detection results. Such techniques for improved background characterization are widely practiced but not well documented or compared. This paper provides a review and comparison of methods in target exclusion, spatial subsetting, and spectral pre-clustering using preliminary matched filter detection results from a larger study. The analysis provides insight into the merit of employing unstructured background characterization techniques, as well as the limitations for their practical application.

show abstract

Section: Spatial Subsettingmentioning

confidence: 99%

Section: Spatial Subsettingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Matched Filter Stochastic Background Characterization for Hyperspectral Target Detection

West¹,

Messinger²,

Ientilucci³

et al. 2005

View full text Add to dashboard Cite

show abstract

“…The corresponding KRX algorithm in the feature space is represented as δ KRXD (Φ(r)) = (Φ(r) −μ BΦ ) TK−1 BΦ (Φ(r) −μ BΦ ) (4) whereμ BΦ andK BΦ are the estimated mean and covariance matrix of the background data in the feature space, respectively. Through certain kernelization and derivation,…”

Section: Kernel Rx Algorithmmentioning

confidence: 99%

“…In HSI anomaly detection, the Reed-Xiaoli (RX) detector of Reed and Yu [4] is widely used and considered a baseline algorithm [5][6][7][8][9][10][11][12]. The well-known RX detector is the benchmark algorithm derived from a generalized likelihood ratio test for an unknown additive contrast signal in a multivariate Gaussian background.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Anomaly Detection Based on a Fast Recursive Kernel RX Algorithm

2016

View full text Add to dashboard Cite

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 in terms of detection accuracy. However, the kernel RX algorithm is computationally more expensive. This paper presents a novel real-time anomaly detection framework based on the kernel RX algorithm. In the kernel RX detector, the inverse covariance matrix and the estimated mean of the background data in the kernel space are non-causal and computationally inefficient. In this work, a local causal sliding array window is used to ensure the causality of the detection system. Using the matrix inversion lemma and the Woodbury matrix identity, both the inverse covariance matrix and estimated mean can be recursively derived without extensive repetitive calculations, and, therefore, the real-time kernel RX detector can be implemented and processed pixel-by-pixel in real time. To substantiate its effectiveness and utility in real-time anomaly detection, real hyperspectral data sets are utilized for experiments.

show abstract

KPCA Algorithm for Hyperspectral Target/Anomaly Detection

2009

Kernel Methods for Remote Sensing Data Analysis

View full text Add to dashboard Cite

Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution

Cited by 1,504 publications

References 6 publications

Matched Filter Stochastic Background Characterization for Hyperspectral Target Detection

Matched Filter Stochastic Background Characterization for Hyperspectral Target Detection

Real-Time Anomaly Detection Based on a Fast Recursive Kernel RX Algorithm

KPCA Algorithm for Hyperspectral Target/Anomaly Detection

Contact Info

Product

Resources

About