&lt;title&gt;Invariant subpixel target identification in hyperspectral imagery&lt;/title&gt;

Thai, Bea; Healey, Glenn

doi:10.1117/12.353034

Cited by 14 publications

(11 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this section, we consider the class of f(x) = (2r)'2IFI112h(d) (1) where d is a quadratic form (Mahalanobis distance) defined by d=(x_t)TF_l(xji) (2) and h(d) is a positive, monotonically decreasing function for all p. We shall denote such a distribution using the shorthand notation EC(t, F, h).…”

Section: Random Vectors With Elliptically Contoured (Ec) Distributionsmentioning

confidence: 99%

<title>Statistics of hyperspectral imaging data</title>

et al. 2001

View full text Add to dashboard Cite

Characterization of the joint (among wavebands) probability density function (pdf) of hyperspectral imaging (HSI) data is crucial for several applications, including the design of constant false alarm rate (CFAR) detectors and statistical classifiers. HSI data are vector (or equivalently multivariate) data in a vector space with dimension equal to the number of spectral bands. As a result, the scalar statistics utilized by many detection and classification algorithms depend upon the joint pdf of the data and the vector-to--scalar mapping defining the specific algorithm. For reasons of analytical tractability, the multivariate Gaussian assumption has dominated the development and evaluation of algorithms for detection and classification in HSI data, although it is widely recognized that it does not always provide an accurate model for the data. The purpose ofthis paper is to provide a detailed investigation ofthejoint and marginal distributional properties of HSI data. To this end, we assess how well the multivanate Gaussian pdf describes HSI data using univariate techniques for evaluating marginal normality, and techniques that use unidimensional views (projections) of multivanate data. We show that the class of elliptically contoured distributions, which includes the multivariate normal distribution as a special case, provides a better characterization of the data. Finally, it is demonstrated that the class of univariate stable random variables provides a better model for the heavy-tailed output distribution of the well known matched filter target detection algorithm.

show abstract

Section: Random Vectors With Elliptically Contoured (Ec) Distributionsmentioning

confidence: 99%

<title>Statistics of hyperspectral imaging data</title>

et al. 2001

View full text Add to dashboard Cite

show abstract

“…The rarity of the target is a very important requirement in this respect; however, it is helpful to remove target-like pixels (that is pixels with large projections |Sjx(n) || onto the target subspace) before computing the eigendecomposition or the SVD. Target leakage can be also reduced by excluding eigenvectors or singular vectors with large projections ||Sfil*|| onto the target subspace [61]. Figure 18 illustrates that a three dimensional subspace can be used to accurately model the spectral variability of the selected target.…”

Section: Subspace or Low-rank Data Modellingmentioning

confidence: 99%

“…Background characterization for the detection of low-probability targets can be done using the eigenvectors [11,56] of the HSI cube correlation matrix R x = X 1 X/N or equivalently the singular vectors [61] of the data matrix X T . In the first case, matrix S b is formed by the first Q significant eigenvectors of R x .…”

Section: Subspace or Low-rank Data Modellingmentioning

confidence: 99%

Detection Algorithms for Hyperspectral Imaging Applications

Manolakis¹

2002

133

View full text Add to dashboard Cite

Detection and identification of military and civilian targets from airborne platforms using hyperspectral sensors is of great interest. Relative to multispectral sensing, hyperspectral sensing can increase the detectability of pixel and subpixel size targets by exploiting finer detail in the spectral signatures of targets and natural backgrounds. A multitude of adaptive detection algorithms for resolved and subpixel targets, with known or unknown spectral characterization, in a background with known or unknown statistics, theoretically justified or ad hoc, with low or high computational complexity, have appeared in the literature or have found their way into software packages and end-user systems. The purpose of this report is twofold. First, we present a unified mathematical treatment of most adaptive matched filter detectors using common notation, and we state clearly the underlying theoretical assumptions. Whenever possible, we express existing ad hoc algorithms as computationally simpler versions of optimal methods. Second, we present a comparative performance analysis of the basic algorithms using theoretically obtained performance characteristics. We focus on algorithms characterized by theoretically desirable properties, practically desired features, or implementation simplicity. Sufficient detail is provided for others to verify and expand this evaluation and framework. A primary goal is to identify best-of-class algorithms for detailed performance evaluation. Finally, we provide a taxonomy of the key algorithms and introduce a preliminary experimental framework for evaluating their performance.

show abstract

“…It has been shown that use of hyperspectral information is useful for detection of objects in military applications such as detecting military vehicles [1,2,23] and mines [3,23], for land use applications [4], and for many USDA product inspection applications [5][6][7][8][9][10][11][12]. This occurs since HS data provides spectral information that uniquely characterizes and identifies the chemical, moisture, and physical properties of the constituent parts of an input object, scene region, or an agricultural product.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral feature selection and fusion for detection of chicken skin tumors

Nakariyakul

Casasent

2004

Monitoring Food Safety, Agriculture, and Plant Health

View full text Add to dashboard Cite

We consider a feature selection method to detect skin tumors on chicken carcasses using hyperspectral reflectance data. This allows for faster data collection than does fluorescence data. A chicken skin tumor is an ulcerous lesion region surrounded by a region of thickened-skin. Detection of chicken tumors is a difficult detection problem because the tumors vary in size and shape; some tumors appear on the side of the chicken. In addition, different areas of normal chicken skin have a variety of hyperspectral response variations, some of which are very similar to the spectral responses of tumors. Similarly, different tumors and different parts of a tumor have different spectral responses. Thus, proper classifier training is needed and many false alarms are expected. Since the spectral responses of the lesion and the thickened-skin regions of tumors are considerably different, we train our feature selection algorithm to detect lesion regions and to detect thickened-skin regions separately; we then process the resultant images and we fuse the two HS detection results to reduce false alarms. Our new forward selection and modified branch and bound algorithm is used to select a small number of λ spectral features that are useful for discrimination. Initial results show that our method offers promise for a good tumor detection rate and a low false alarm rate.

show abstract

<title>Invariant subpixel target identification in hyperspectral imagery</title>

Cited by 14 publications

References 5 publications

<title>Statistics of hyperspectral imaging data</title>

<title>Statistics of hyperspectral imaging data</title>

Detection Algorithms for Hyperspectral Imaging Applications

Hyperspectral feature selection and fusion for detection of chicken skin tumors

Contact Info

Product

Resources

About