Development of a Web-Based Application to Evaluate Target Finding Algorithms

Snyder, David K.; Kerekes, John P.; Fairweather, Ian S.; Crabtree, Robert L.; Shive, Joshua; Hager, Sven

doi:10.1109/igarss.2008.4779144

Cited by 154 publications

(69 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This study considered three separate hyperspectral images: the classic AVIRIS 25, 26 image of Indian Pines, the HyMap blind test 27 image of Cooke City, and a SpecTIR image of Reno, Nevada.…”

Section: Resultsmentioning

confidence: 99%

“…SNR is the signal-to-noise ratio, in decibels, that corresponds to the variance of the original image divided by the variance of the residuals; it is defined in Eq. (27). LVR is the log-volume-ratio corresponds to log(V o /V ), where V o is the volume of the ellipsoid corresponding to the covariance of the original data, and V is the volume of the covariance ellipsoid of the residual data; it can be computed using the formula in Eq.…”

Section: Extension To Multiple Spectral Channelsmentioning

confidence: 99%

See 1 more Smart Citation

Symmetrized regression for hyperspectral background estimation

Theiler

2015

SPIE Proceedings

View full text Add to dashboard Cite

We can improve the detection of targets and anomalies in a cluttered background by more effectively estimating that background. With a good estimate of what the target-free radiance or reflectance ought to be at a pixel, we have a point of comparison with what the measured value of that pixel actually happens to be. It is common to make this estimate using the mean of pixels in an annulus around the pixel of interest. But there is more information in the annulus than this mean value, and one can derive more general estimators than just the mean. The derivation pursued here is based on multivariate regression of the central pixel against the pixels in the surrounding annulus. This can be done on a band-by-band basis, or with multiple bands simultaneously.For overhead remote sensing imagery with square pixels, there is a natural eight-fold symmetry in the surrounding annulus, corresponding to reflection and right angle rotation. We can use this symmetry to impose constraints on the estimator function, and we can use these constraints to reduce the number or regressor variables in the problem. This paper investigates the utility of regression generally -and a variety of different symmetric regression schemes particularly -for hyperspectral background estimation in the context of generic target detection.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Extension To Multiple Spectral Channelsmentioning

confidence: 99%

Symmetrized regression for hyperspectral background estimation

Theiler

2015

SPIE Proceedings

View full text Add to dashboard Cite

show abstract

“…The hyperspectral imagery was collected by the HyMap sensor operated by HyVista 2 . The georegistered HyMap imagery has approximately 3 meter ground resolution and it is available both in calibrated spectral radiance as well as in spectral reflectance after atmospheric compensation [15]. Fig.…”

Section: Methodsmentioning

confidence: 99%

Binary partition trees-based robust adaptive hyperspectral RX anomaly detection

Veganzones

Frontera-Pons

Pascal

et al. 2014

2014 IEEE International Conference on Image Processing (ICIP)

View full text Add to dashboard Cite

The Reed-Xiaoli (RX) is considered as the benchmark algorithm in multidimensional anomaly detection (AD). However, the RX detector performance decreases when the statistical parameters estimation is poor. This could happen when the background is non-homogeneous or the noise independence assumption is not fulfilled. For a better performance, the statistical parameters are estimated locally using a sliding window approach. In this approach, called adaptive RX, a window is centered over the pixel under the test (PUT), so the background mean and covariance statistics are estimated using the data samples lying inside the window's spatial support, named the secondary data. Sometimes, a smaller guard window prevents those pixels close to the PUT to be used, in order to avoid the presence of outliers in the statistical estimation. The size of the window is chosen large enough to ensure the invertibility of the covariance matrix and small enough to justify both spatial and spectral homogeneity. We present here an alternative methodology to select the secondary data for a PUT by means of a binary partition tree (BPT) representation of the image. We test the proposed BPT-based adaptive hyperspectral RX AD algorithm using a real dataset provided by the Target Detection Blind Test project.

show abstract

“…The second dataset was acquired by the HyMap airborne hyperspectral imaging sensor, 31 which provides 126 spectral bands spanning the wavelength interval 0.4 to 2.5 μm. The image dataset, covering one area of Cooke City, Montana, was collected on July 4, 2006, with the spatial size 200 × 800 pixels.…”

Section: Hyperspectral Datamentioning

confidence: 99%

Decision fusion for dual-window-based hyperspectral anomaly detector

2015

J. Appl. Remote Sens

View full text Add to dashboard Cite

Abstract. In hyperspectral anomaly detection, the dual-window-based detector is a widely used technique that employs two windows to capture nonstationary statistics of anomalies and background. However, its detection performance is usually sensitive to the choice of window sizes and suffers from inappropriate window settings. In this work, a decision-fusion approach is proposed to alleviate such sensitivity by merging the results from multiple detectors with different window sizes. The proposed approach is compared with the classic Reed-Xiaoli (RX) algorithm as well as kernel RX (KRX) using two real hyperspectral data. Experimental results demonstrate that it outperforms the existing detectors, such as RX, KRX, and multiple-window-based RX. The overall detection framework is suitable for parallel computing, which can greatly reduce computational time when processing large-scale remote sensing image data. © The Authors.Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

show abstract

Development of a Web-Based Application to Evaluate Target Finding Algorithms

Cited by 154 publications

References 1 publication

Symmetrized regression for hyperspectral background estimation

Symmetrized regression for hyperspectral background estimation

Binary partition trees-based robust adaptive hyperspectral RX anomaly detection

Decision fusion for dual-window-based hyperspectral anomaly detector

Contact Info

Product

Resources

About