[PDF] Top 20 Robust joint sparsity model for hyperspectral image classification

... the joint sparse representation classification (JSRC) in HSI classification by incorporating spatial ...The model was based on the observation that the pixels in a patch share similar spectral ... See full document

... sparse classification, it suffers from inefficacy in dealing with high dimensional features and manually set initial regressor ...for hyperspectral image (HSI) ...effective classification of ... See full document

... high classification performance, not only is the development of sparse representation models essential, the designing and learning of quality dictio- naries also plays an important ...certain sparsity, as ... See full document

... Second, some spectral-spatial classifications take spa- tial information into the classifier during classification. Simultaneous orthogonal matching pursuit (SOMP) and simultaneous subspace pursuit (SSP) [18] ... See full document

... HSI classification based on a locality-preserving dimensionality-reduction step—local Fisher’s discriminant analysis (LFDA)—as well as a Gaussian- mixture-model (GMM) ...for hyperspectral ... See full document

... sparse classification, it suffers from inefficacy in dealing with high dimensional features and manually set initial regressor ...for hyperspectral image (HSI) ...effective classification of ... See full document

... most robust behavior when dealing with different input spatial sizes (see Tables VI and ...the model to reduce the declining- accuracy phenomenon when considering significantly deep ... See full document

... the hyperspectral face recognition by extracting spectral features picked from typical face regions, such as forehead, cheeks, hair, and ...is robust to the orientation and expression varia- ...on ... See full document

... improved classification performance of CSCSVM over SDL for all three data sets ...comparable classification performances we obtained for these meth- ods are indicative that similar discriminative character ... See full document

... of classification, recent studies have suggested incorporating spatial information into a spectral-based classifier [10], which is called the spectral-spatial HSI ...HSI classification. Various types of ... See full document

... for hyperspectral images are based on the well-known linear discriminant analysis (LDA) method, which uses labelled samples to fi nd a projection matrix that maximizes between-class variance to within-class ... See full document

... for hyperspectral imagery classification and ...of image processing, computer vision, and pattern ...or sparsity works and why one of them alone is not sufficient, and then analyze how CR and ... See full document

... A hyperspectral image (HSI) is a 3-dimensional data cube with two spatial dimensions and one spectral ...sparse model can be approximately solved by greedy algorithms such as orthogonal matching ... See full document

... In this context, we believe that, in order to develop a dictionary with high discriminative power for HSI classi fi cation but from only a limited number of labelled training samples, it is a promising direction to ... See full document

... the hyperspectral data, it is difficult to design accurate and efficient image segmentation algorithms for such ...of hyperspectral and multispectral ...in image segmentation ...the ... See full document

... In that paper, the Gaussian Mixture Model (GMM) was implemented to select important features before being processed in the forward feature selection. The results of this study are compared with the feature ... See full document

... The vast variety of available sensors, which provide data either in image or signal formats, allows to tackle a large number of applications with remark- able advantages. In general, each family of sensors is ... See full document

... of Hyperspectral Image Classification Thus, hyperspectral imaging is concerned with the measurement, processing and analysis of spectra acquired from a given scene at a short, medium or long ... See full document

... final classification results, the base classifiers deci- sion values or probabilistic scores are combined using three popular combination functions such as majority voting (MV), Bayesian average (Avg), and product ... See full document

... presents hyperspectral image classification using genetic algorithm after visualization using image fusion ...technique. Hyperspectral remote sensors collect image data for a ... See full document