Is there a best hyperspectral detection algorithm?

Manolakis, Dimitris G.; Lockwood, Ronald B.; Cooley, T.; Jacobson, J.

doi:10.1117/12.816917

Cited by 151 publications

(96 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The common hyperspectral detection algorithms include orthogonal subspace projection (OSP) [22][23][24], constrained energy minimization (CEM) [20,22], and matched filter (MF) [21,[25][26][27][28][29][30][31][32]. The OSP uses the linear mixture model and white Gaussian noise assumption.…”

Section: Introductionmentioning

confidence: 99%

Target Detection Method for Water Mapping Using Landsat 8 OLI/TIRS Imagery

Geng

Sun

et al. 2015

Water

View full text Add to dashboard Cite

Extracting surface water distribution with satellite imagery has been an important subject in remote sensing. Spectral indices of water only use information from a limited number of bands, thus they may have poor performance from pixels contaminated by ice/snow, clouds, etc. The detection algorithms using information from all spectral bands, such as constrained energy minimization (CEM), could avoid this problem to some extent. However, these are mostly designed for hyperspectral imagery, and may fail when applied to multispectral data. It has been proved that adding linearly irrelevant data to original data could improve the performance of CEM. In this study, two kinds of linearly irrelevant data are added for water extraction: the spectral indices and the spectral similarity metric data. CEM is designed for targets with low-probability distribution in an image, but water bodies do not always satisfy this condition. We thereby impose a sensible coefficient for each pixel to form the weighted autocorrelation matrix. In this study, the weight is based on the orthogonal subspace projection, so this new method is named Orthogonal subspace projection Weighted CEM (OWCEM). The newly launched Landsat 8 images over two lakes, the Hala Lake in China with ice/snow distributed in the north, and the Huron Lake in OPEN ACCESSWater 2015, 7 795North America, a lake with a very large surface area, are selected to test the accuracy and robustness of our algorithm. The Kappa coefficient and the receiver operating characteristic (ROC) curve are calculated as an accuracy evaluation standard. For both lakes, our method can greatly suppress the background (including ice/snow and clouds) and extract the complete water surface with a high accuracy (Kappa coefficient > 0.96).

show abstract

Section: Introductionmentioning

confidence: 99%

Target Detection Method for Water Mapping Using Landsat 8 OLI/TIRS Imagery

Geng

Sun

et al. 2015

Water

View full text Add to dashboard Cite

show abstract

“…The spectral signature of the target of interest is determined from a spectral library or the mean of a sample of known target pixels collected under the same conditions. 3 …”

Section: Classification Algorithmsmentioning

confidence: 99%

“…This statement is contested in Smetek,5 where the potential ill effects of a small number of anomalies on the estimation of the covariance matrix are detailed. Similarly, Manolakis et al 3 state that:…”

Section: Introductionmentioning

confidence: 99%

“…Some approaches to attain this objective include: 1. run a detection algorithm, remove a set of pixels that score high, recompute the covariance with the remaining pixels, and "re-run" the detection algorithm and 2. before computing the covariance, remove the pixels with high projections onto the target subspace. 3 This article demonstrates that classification algorithms, such as the adaptive matched filter (AMF), may be improved by addressing spatial correlation and homogeneity problems inherent to HSI that are often ignored in practice. We begin by showing the benefit of using an anomaly detector to remove potential anomalies from the mean vector and covariance matrix statistics, as suggested by Manolakis et al 3 In addition, we show further benefits by addressing the nonhomogeneity of HSI through the use of cluster analysis prior to classification.…”

Section: Introductionmentioning

confidence: 99%

“…3 This article demonstrates that classification algorithms, such as the adaptive matched filter (AMF), may be improved by addressing spatial correlation and homogeneity problems inherent to HSI that are often ignored in practice. We begin by showing the benefit of using an anomaly detector to remove potential anomalies from the mean vector and covariance matrix statistics, as suggested by Manolakis et al 3 In addition, we show further benefits by addressing the nonhomogeneity of HSI through the use of cluster analysis prior to classification.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Clustering hyperspectral imagery for improved adaptive matched filter performance

Williams¹,

Bauer²,

Friend³

2013

J. Appl. Remote Sens

View full text Add to dashboard Cite

Abstract. This paper offers improvements to adaptive matched filter (AMF) performance by addressing correlation and non-homogeneity problems inherent to hyperspectral imagery (HSI). The estimation of the mean vector and covariance matrix of the background should be calculated using "target-free" data. This statement reflects the difficulty that including target data in estimates of the meanvector and covariance matrix of the background could entail. This data could act as statistical outliers and severely contaminate the estimators. This fact serves as the impetus for a 2-stage process: First, attempt to remove the target data from the background by way of the employment of anomaly detectors. Next, with remaining data being relatively "target-free" the way is cleared for signature matching. Relative to the first stage, we were able to test seven different anomaly detectors, some of which are designed specifically to deal with the spatial correlation of HSI data and/or the presence of anomalous pixels in local or global mean and covariance estimators. Relative to the second stage, we investigated the use of cluster analytic methods to boost AMF performance. The research shows that accounting for spatial correlation effects in the detector yields nearly "target-free" data for use in an AMF that is greatly benefitted through the use of cluster analysis methods.

show abstract

Classification of forensic hyperspectral paper data using hybrid spectral similarity algorithms

Deepthi

Devassy

George

et al. 2022

Journal of Chemometrics

View full text Add to dashboard Cite

Document forgeries that involve modification of the materials used, such as ink and paper, provide evidence of any malpractices being performed. Forensic specialists use different techniques to identify and classify these samples; however, the most preferred method is to use nondestructive techniques to avoid any potential damage to the original specimen under investigation. Hyperspectral imaging has already been explored in several application domains and used as a powerful method in forensic investigations to extract information about the materials under examination. To precisely classify the material information and utilize the hyperspectral imaging technique's potential, we probed the potential of some hybrid spectral similarity measures to classify different commonly used paper samples. A comparison of these methods is quantitatively presented in this article.

show abstract

Is there a best hyperspectral detection algorithm?

Cited by 151 publications

References 29 publications

Target Detection Method for Water Mapping Using Landsat 8 OLI/TIRS Imagery

Target Detection Method for Water Mapping Using Landsat 8 OLI/TIRS Imagery

Clustering hyperspectral imagery for improved adaptive matched filter performance

Classification of forensic hyperspectral paper data using hybrid spectral similarity algorithms

Contact Info

Product

Resources

About