An oblique subspace projection approach for mixed pixel classification in hyperspectral images

Tu, Te-Ming; Shyu, Hsuen-Chyun; Lee, Ching-Hai; Chang, Chein‐I

doi:10.1016/s0031-3203(98)00165-4

Cited by 15 publications

(8 citation statements)

References 9 publications

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“…Then in model (1) can be expressed as a Gaussian distribution with mean and variance (i.e., ). The MLE of for model (5) can be obtained in [23], [24] and [29] by (26) In particular, the estimate of the -th abundance is given by (27) and the associated estimation error is (28) From (6) and (26), SSC and MLE both generate an identical abundance estimate , but different noise estimates are produced, for SSC in (16), and for MLE in (28). However, if we further compare (24) to (27) and (25) to (28), we discover that both sets of equations are identical.…”

Section: A a Posteriori Orthogonal Subspace Projectionmentioning

confidence: 99%

“…Apply the OSP classifier given by (4) to classify , where is the undesired signature matrix made up of all signatures in except for the desired signature . It is worth noting that the OPCI stopping criterion given by (29), actually arises from the constant appearing in the estimation errors derived in (16), (25) and (28). One comment on OPCI is useful regarding implementation of ATDCA.…”

Section: Stage 2) Target Classification Process (Tcp)mentioning

confidence: 99%

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An experiment-based quantitative and comparative analysis of target detection and image classification algorithms for hyperspectral imagery

Chang

Ren

2000

IEEE Trans. Geosci. Remote Sensing

103

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Abstract-Over the past years, many algorithms have been developed for multispectral and hyperspectral image classification. A general approach to mixed pixel classification is linear spectral unmixing, which uses a linear mixture model to estimate the abundance fractions of signatures within a mixed pixel. As a result, the images generated for classification are usually gray scale images, where the gray level value of a pixel represents a combined amount of the abundance of spectral signatures residing in this pixel. Due to a lack of standardized data, these mixed pixel algorithms have not been rigorously compared using a unified framework. In this paper, we present a comparative study of some popular classification algorithms through a standardized HYDICE data set with a custom-designed detection and classification criterion. The algorithms to be considered for this study are those developed for spectral unmixing, the orthogonal subspace projection (OSP), maximum likelihood, minimum distance, and Fisher's linear discriminant analysis (LDA). In order to compare mixed pixel classification algorithms against pure pixel classification algorithms, the mixed pixels are converted to pure ones by a designed mixed-to-pure pixel converter. The standardized HYDICE data are then used to evaluate the performance of various pure and mixed pixel classification algorithms. Since all targets in the HYDICE image scenes can be spatially located to pixel level, the experimental results can be presented by tallies of the number of targets detected and classified for quantitative analysis.Index Terms-Linear discriminant analysis (LDA), linear unmixing, maximum likelihood estimator (MLE), minimum distance, mixed-to-pure pixel (M/P) converter (M/P converter), oblique subspace projection (OBSP), orthogonal subspace projection (OSP), signature space projection (SSP), winner-take-all M/P converter (WTAMPC).

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Section: A a Posteriori Orthogonal Subspace Projectionmentioning

confidence: 99%

Section: Stage 2) Target Classification Process (Tcp)mentioning

confidence: 99%

An experiment-based quantitative and comparative analysis of target detection and image classification algorithms for hyperspectral imagery

Chang

Ren

2000

IEEE Trans. Geosci. Remote Sensing

103

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“…In order to find a vector which maximizes the SNR defined by (15), we apply to (22) and obtain (23) where due to…”

Section: Least Squares Subspace Projectionmentioning

confidence: 99%

“…For example, an analysis of the estimation errors resulting from LSOSP is investigated in [21]. Also, an oblique subspace projection approach is proposed in [19], [20], [22] as an alternative to LSOSP where the oblique subspace projection is not necessarily orthogonal as is LSOSP.…”

Section: B Experiments 2 (Locally Optimal N-p Detection)mentioning

confidence: 99%

A posteriori least squares orthogonal subspace projection approach to desired signature extraction and detection

Chen

Chang

1997

IEEE Trans. Geosci. Remote Sensing

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One of the primary goals of imaging spectrometry in earth remote sensing applications is to determine identities and abundances of surface materials. In a recent study, an orthogonal subspace projection (OSP) was proposed for image classification. However, it was developed for an a priori linear spectral mixture model which did not take advantage of a posteriori knowledge of observations. In this paper, an a posterior least squares orthogonal subspace projection (LSOSP) derived from OSP is presented on the basis of an a posteriori model so that the abundances of signatures can be estimated through observations rather than assumed to be known as in the a priori model. In order to evaluate the OSP and LSOSP approaches, a Neyman-Pearson detection theory is developed where a receiver operating characteristic (ROC) curve is used for performance analysis. In particular, a locally optimal Neyman-Pearson's detector is also designed for the case where the global abundance is very small with energy close to zero a case to which both LSOSP and OSP cannot be applied. It is shown through computer simulations that the presented LSOSP approach significantly improves the performance of OSP.Index Terms-A priori (pr); A posterior (ps); detection power, false alarm probability, least-squares estimate, Neyman-Pearson (N-P) detectors, orthogonal subspace projection, ROC curve.

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“…Among the applications we can find in the literature we list the work from Behrens and Scharf (1994), who presents the use of ObSP for interpolation, decoding, and elimination of symbol interferences in a communication channel. Tu et al (1999), used ObSP for hyperspectral image classification. They applied ObSP to quantify the mixture of spectral signatures, from different materials, contained in a specific pixel of an hyperspectral image.…”

Section: Introductionmentioning

confidence: 99%

Decomposition of Near-Infrared Spectroscopy Signals Using Oblique Subspace Projections: Applications in Brain Hemodynamic Monitoring

et al. 2016

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Clinical data is comprised by a large number of synchronously collected biomedical signals that are measured at different locations. Deciphering the interrelationships of these signals can yield important information about their dependence providing some useful clinical diagnostic data. For instance, by computing the coupling between Near-Infrared Spectroscopy signals (NIRS) and systemic variables the status of the hemodynamic regulation mechanisms can be assessed. In this paper we introduce an algorithm for the decomposition of NIRS signals into additive components. The algorithm, SIgnal DEcomposition base on Obliques Subspace Projections (SIDE-ObSP), assumes that the measured NIRS signal is a linear combination of the systemic measurements, following the linear regression model y = Ax + ϵ. SIDE-ObSP decomposes the output such that, each component in the decomposition represents the sole linear influence of one corresponding regressor variable. This decomposition scheme aims at providing a better understanding of the relation between NIRS and systemic variables, and to provide a framework for the clinical interpretation of regression algorithms, thereby, facilitating their introduction into clinical practice. SIDE-ObSP combines oblique subspace projections (ObSP) with the structure of a mean average system in order to define adequate signal subspaces. To guarantee smoothness in the estimated regression parameters, as observed in normal physiological processes, we impose a Tikhonov regularization using a matrix differential operator. We evaluate the performance of SIDE-ObSP by using a synthetic dataset, and present two case studies in the field of cerebral hemodynamics monitoring using NIRS. In addition, we compare the performance of this method with other system identification techniques. In the first case study data from 20 neonates during the first 3 days of life was used, here SIDE-ObSP decoupled the influence of changes in arterial oxygen saturation from the NIRS measurements, facilitating the use of NIRS as a surrogate measure for cerebral blood flow (CBF). The second case study used data from a 3-years old infant under Extra Corporeal Membrane Oxygenation (ECMO), here SIDE-ObSP decomposed cerebral/peripheral tissue oxygenation, as a sum of the partial contributions from different systemic variables, facilitating the comparison between the effects of each systemic variable on the cerebral/peripheral hemodynamics.

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An oblique subspace projection approach for mixed pixel classification in hyperspectral images

Cited by 15 publications

References 9 publications

An experiment-based quantitative and comparative analysis of target detection and image classification algorithms for hyperspectral imagery

An experiment-based quantitative and comparative analysis of target detection and image classification algorithms for hyperspectral imagery

A posteriori least squares orthogonal subspace projection approach to desired signature extraction and detection

Decomposition of Near-Infrared Spectroscopy Signals Using Oblique Subspace Projections: Applications in Brain Hemodynamic Monitoring

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