[PDF] Top 20 Singular spectrum analysis for effective feature extraction in hyperspectral imaging
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Singular spectrum analysis for effective feature extraction in hyperspectral imaging
... series analysis, Singular Spectrum Analysis (SSA) has been applied in many diverse areas, where an original 1D signal can be decomposed into a sum of components including varying trends, ... See full document
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Fast implementation of singular spectrum analysis for effective feature extraction in hyperspectral imaging
... series analysis, singular spectrum analysis (SSA) has been successfully applied for feature extraction in hyperspectral imaging (HSI), leading to increased accuracy ... See full document
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Singular spectrum analysis for improving hyperspectral imaging based beef eating quality evaluation
... 3 Singular spectrum analysis for improving hyperspectral imaging based beef eating quality evaluation 4 Tong Qiaoa, Jinchang Renb, Cameron Craigiec,d, Jaime Zabalzae, Charlotte Maltinc,f[r] ... See full document
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Effective feature extraction and data reduction with hyperspectral imaging in remote sensing
... for feature extraction and data reduction, it suffers from three main drawbacks: high computational cost, large memory requirement and low efficacy in processing large datasets such as ...similar ... See full document
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Joint bilateral filtering and spectral similarity-based sparse representation : a generic framework for effective feature extraction and data classification in hyperspectral imaging
... of hyperspectral images (HSI) has been a challenging problem under active investigation for years especially due to the extremely high data dimensionality and limited number of samples available for ...that ... See full document
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Fast implementation of two-dimensional singular spectrum analysis for effective data classification in hyperspectral imaging
... the singular spectrum analysis (SSA) [7] technique for feature extraction in HSI remote ...for feature extraction in the spectral domain (applied to pixels) as 1D-SSA [8], ... See full document
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Novel two dimensional singular spectrum analysis for effective feature extraction and data classification in hyperspectral imaging
... applications, feature extraction and dimension reduction in HSI has been intensively investigated in the last ...component analysis (PCA) [9] and several variants [10-12], maximum noise fraction ... See full document
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Novel two dimensional singular spectrum analysis for effective feature extraction and data classification in hyperspectral imaging
... These methods and parameters used are summarized in Table I, where the Baseline case is straight as it requires no parameters, while for the 1D-SSA method we use the same configuration implemented for the main results in ... See full document
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Novel segmented stacked autoencoder for effective dimensionality reduction and feature extraction in hyperspectral imaging
... on feature representation include widely known classical techniques and, on the other hand, more modern ...component analysis (PCA) [5], independent component analysis (ICA) [6], or maximum noise ... See full document
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Structured covariance principal component analysis for real-time onsite feature extraction and dimensionality reduction in hyperspectral imaging
... Theoretical analysis and exper- imental results have verified the equivalency of the SC-PCA approaches to the conventional ...other feature extraction algorithms, such as singular ... See full document
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Local block multilayer sparse extreme learning machine for effective feature extraction and classification of hyperspectral images
... for feature extraction, such as principal component analysis (PCA) and its variations [14-16], segmented auto-encoder [17] and singular spectrum analysis (SSA) ...for ... See full document
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Effective denoising and classification of hyperspectral images using curvelet transform and singular spectrum analysis
... more effective feature ...spectral feature extraction techniques, the proposed method always stands out with the highest classification ...and feature extraction method is either ... See full document
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Local block multilayer sparse extreme learning machine for effective feature extraction and classification of hyperspectral images
... for feature extraction, such as principal component analysis (PCA) and its variations [14-16], segmented auto-encoder [17] and singular spectrum analysis (SSA) ...for ... See full document
15
Novel folded-PCA for improved feature extraction and data reduction with hyperspectral imaging and SAR in remote sensing
... structure extraction, the effect on the classification results of the parameter H is compared in ...in effective feature extraction and data ... See full document
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Effective classification of Chinese tea samples in hyperspectral imaging
... PCA is a method used to transform correlated data into a number of uncorrelated variables, known as principal components [14]. The first principal component contains as much of the variance of the data as possible; each ... See full document
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Hyperspectral Imaging System Model Implementation and Analysis
... the feature extraction due to the high dimensionality of the original data, which is always hundreds of channels for hyperspectral ...in spectrum such as visible region, VNIR, SWIR, etc, or a ... See full document
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Dimensionality reduction based on determinantal point process and singular spectrum analysis for hyperspectral images
... and feature extraction methods have their own advantages and disadvantages for dimensionality ...contrary, feature extraction can effectively use the potential features of the ... See full document
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Hyperspectral image spectral spatial feature extraction via tensor principal component analysis
... YPERSPECTRAL imaging sensors collect hyperspectral images in the form of 3D arrays, with two spatial dimensions representing the image width and height, and a spectral dimension describing the spectral ... See full document
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Land Mapping Based On Hyperspectral Image Feature Extraction Using Guided Filter
... of hyperspectral data should increase the abilities and effectiveness in classifying land mapping/cover ...as effective as hyperspectral ...the hyperspectral case, the estimation of ... See full document
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Land Mapping Based On Hyperspectral Image Feature Extraction Using Guided Filter
... Component Analysis (PCA) [2], Minimum Noise fraction (MNF) [3],Linear Discriminant Analysis (LDA) [4], Linear Spectral mixture analysis (LSMA) [5] , Wavelet Based methods [6], Independent Component ... See full document
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