Independent component analysis-based dimensionality reduction with applications in hyperspectral image analysis

By Jing Wang, Chein-I. Chang. IEEE Transactions on Geoscience and Remote Sensing, 2006

636 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Media Technology, Signal Processing

View Paper: https://www.semanticscholar.org/paper/f2113ee499e607d94bb0a51b5bfcbbb78e679c1a

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In Analytical Chemistry

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In Signal Processing

Citations in the corpus

A Novel Ranking-Based Clustering Approach for Hyperspectral Band Selection (2016, methodology): “as projection pursuit [13], [14], principal component analysis (PCA) [15]–[17], and independent component analysis [18], whereas the latter identifies a subset of the original spectral bands that contains most of the characteristics [19].”
Constrained band selection for hyperspectral imagery (2006, methodology): “It has been shown in [20] that the value of the VD could be also used as the number of endmembers required to be generated.”
Hyperspectral Subspace Identification (2008, methodology): “Independent component analysis [32], [33], projection pursuit [34], [35], and wavelet decomposition [36], [37] have also been considered.”
Clustering-Based Hyperspectral Band Selection Using Information Measures (2007, methodology): “Digital Object Identifier 10.1109/TGRS.2007.904951 This reduction in the hyperspectral representation could be done using feature extraction [1]–[4] or feature selection techniques [5], [6].”
Segmentation and classification of hyperspectral images using watershed transformation (2010, methodology): “Before computing a gradient, feature extraction on the original image can be performed, applying one of the transformations such as the principal component analysis (PCA) [52,53], maximum noise fraction (MNF) [54] and independent component analysis (ICA) [55,56].”
A New Growing Method for Simplex-Based Endmember Extraction Algorithm (2006, methodology): “Since similar tables using the SAM for signature matching can be obtained in the exact same manner that was performed in [17], the results are not included here to avoid replication.”
Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches (2012, neutral): “Independent component analysis [108], [109], projection pursuit [110], [111], and wavelet decomposition [112], [113] have also been considered.”
Spectral–Spatial Classification of Hyperspectral Imagery Based on Partitional Clustering Techniques (2009, neutral): “In previous studies, the feature extraction methods appropriate for hyperspectral image analysis, such as the PCA, the ICA, the ISOMAP and clustering-based band selection were considered [39], [42], [50], [51].”
Salient Band Selection for Hyperspectral Image Classification via Manifold Ranking (2016, methodology): “But differently, feature extraction techniques transform the original high-dimensional data to low-dimensional data through certain kinds of projections, such as principle component analysis [11], independent component analysis [12], and discrete wavelet”