Robust Hyperspectral Classification Using Relevance Vector Machine

Mianji, Fereidoun A.; Zhang, Y

doi:10.1109/tgrs.2010.2103381

Cited by 108 publications

(39 citation statements)

References 60 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We also conducted an experiment wherein we varied the amount of training data and studied the sensitivity of the proposed methods relative to conventional methods over a range of training-data set sizes [34]. In practical situations, the number of training samples available is often insufficient to estimate models for each class.…”

Section: Comparison Against Current State-of-the-art Parametric CLmentioning

confidence: 99%

Locality-Preserving Dimensionality Reduction and Classification for Hyperspectral Image Analysis

Prasad

Fowler

et al. 2012

IEEE Trans. Geosci. Remote Sensing

433

205

View full text Add to dashboard Cite

Abstract-Hyperspectral imagery typically provides a wealth of information captured in a wide range of the electromagnetic spectrum for each pixel in the image; however, when used in statistical pattern-classification tasks, the resulting high-dimensional feature spaces often tend to result in ill-conditioned formulations. Popular dimensionality-reduction techniques such as principal component analysis, linear discriminant analysis, and their variants typically assume a Gaussian distribution. The quadratic maximumlikelihood classifier commonly employed for hyperspectral analysis also assumes single-Gaussian class-conditional distributions. Departing from this single-Gaussian assumption, a classification paradigm designed to exploit the rich statistical structure of the data is proposed. The proposed framework employs local Fisher's discriminant analysis to reduce the dimensionality of the data while preserving its multimodal structure, while a subsequent Gaussian mixture model or support vector machine provides effective classification of the reduced-dimension multimodal data. Experimental results on several different multiple-class hyperspectral-classification tasks demonstrate that the proposed approach significantly outperforms several traditional alternatives.Index Terms-Dimensionality reduction, Gaussian-mixturemodel (GMM), hyperspectral data, local discriminant analysis, support vector machine.

show abstract

Section: Comparison Against Current State-of-the-art Parametric CLmentioning

confidence: 99%

Locality-Preserving Dimensionality Reduction and Classification for Hyperspectral Image Analysis

Prasad

Fowler

et al. 2012

IEEE Trans. Geosci. Remote Sensing

433

205

View full text Add to dashboard Cite

show abstract

“…Nonlinear kernels may also be used within the SVM framework to achieve nonlinear separation in the feature space via linear separation in the kernel-induced space. Variations of the SVM (e.g., [3], [13]) have been proposed to further improve classification performance. For example, in [13], locality Fisher's discriminant analysis (LFDA) was employed to reduce the dimensionality of hyperspectral data for the SVM classifier.…”

Section: Introductionmentioning

confidence: 99%

Nearest Regularized Subspace for Hyperspectral Classification

Tramel

Prasad

et al. 2014

IEEE Trans. Geosci. Remote Sensing

213

View full text Add to dashboard Cite

Abstract-A classifier that couples nearest-subspace classification with a distance-weighted Tikhonov regularization is proposed for hyperspectral imagery. The resulting nearest-regularizedsubspace classifier seeks an approximation of each testing sample via a linear combination of training samples within each class. The class label is then derived according to the class which best approximates the test sample. The distance-weighted Tikhonov regularization is then modified by measuring distance within a locality-preserving lower-dimensional subspace. Furthermore, a competitive process among the classes is proposed to simplify parameter tuning. Classification results for several hyperspectral image data sets demonstrate superior performance of the proposed approach when compared to other, more traditional classification techniques.

show abstract

“…SVM has been employed for solving classification and forecasting problem like [6]. RVM model also can be applied for solving classification problem successfully such as [7,8]. The most compelling feature of the RVM is that, while capable of generalization performance comparable to an equivalent SVM, it typically utilizes dramatically fewer kernel functions.…”

Section: Rvm Algorithmmentioning

confidence: 99%

A Prediction Model Based on Relevance Vector Machine and Granularity Analysis

Cho

2016

IJFIS

View full text Add to dashboard Cite

In this paper, a yield prediction model based on relevance vector machine (RVM) and a granular computing model (quotient space theory) is presented. With a granular computing model, massive and complex meteorological data can be analyzed at different layers of different grain sizes, and new meteorological feature data sets can be formed in this way. In order to forecast the crop yield, a grey model is introduced to label the training sample data sets, which also can be used for computing the tendency yield. An RVM algorithm is introduced as the classification model for meteorological data mining. Experiments on data sets from the real world using this model show an advantage in terms of yield prediction compared with other models.

show abstract

Robust Hyperspectral Classification Using Relevance Vector Machine

Cited by 108 publications

References 60 publications

Locality-Preserving Dimensionality Reduction and Classification for Hyperspectral Image Analysis

Locality-Preserving Dimensionality Reduction and Classification for Hyperspectral Image Analysis

Nearest Regularized Subspace for Hyperspectral Classification

A Prediction Model Based on Relevance Vector Machine and Granularity Analysis

Contact Info

Product

Resources

About