Classification of Hyperspectral Images With Regularized Linear Discriminant Analysis

By Tatyana V. Bandos Marsheva, Lorenzo Bruzzone, Gustau Camps-Valls. IEEE Transactions on Geoscience and Remote Sensing, 2009

612 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Atmospheric Science, Media Technology

View Paper: https://www.semanticscholar.org/paper/558593f2ba9a6c921dcef904d4f2b1e87b9084f7

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Citations in the corpus

Spectral–Spatial Feature Tokenization Transformer for Hyperspectral Image Classification (2022, methodology): “In addition, several methods for dimension reduction and spectral information extraction have also been developed, including the principal component analysis (PCA) [22], [23], the independent component analysis (ICA) [24], and the linear discriminant analysis (LDA) [25]–[27].”
Feature Extraction for Hyperspectral Imagery: The Evolution From Shallow to Deep: Overview and Toolbox (2020, methodology): “To further enhance the class separability, a large number of extended methods have been successfully proposed in recent years, which are subspace LDA (SLDA) [56], regularized LDA [57], local fisher’s discriminant analysis (LFDA) [58], feature space discriminative analysis (FSDA) [59], rough-set-base”
Deep Learning for Hyperspectral Image Classification: An Overview (2019, methodology): “In addition, some other classification approaches have focused on designing an effective feature-extraction or dimensionreduction technique, such as principle component analysis (PCA) [13], [14], independent component analysis (ICA) [15], and linear discriminant analysis (LDA) [16].”
Semisupervised Neural Networks for Efficient Hyperspectral Image Classification (2010, methodology): “be regularized to obtain a proper estimation of the sample covariance matrix [23], [24].”
Advances in Hyperspectral Image Classification: Earth Monitoring with Statistical Learning Methods (2013, methodology): “For that reason, research turned to include regularization, either explicitly through Tikhonov’s terms in the involved covariance matrices, or by perform classification in a subspace of reduced dimensionality [4, 5].”
Feature Mining for Hyperspectral Image Classification (2013, methodology): “Nonparametric FE via regularization techniques has also been proposed to overcome the limitations of LDA and obtain more stable results, particularly for SSS problems [16], [36], [110].”
Manifold-Learning-Based Feature Extraction for Classification of Hyperspectral Data: A Review of Advances in Manifold Learning (2014, neutral): “locality pReseRvinG DiscRiminative Dimensionality ReDuction PCA, linear discriminant analysis (LDA), and their many variants, such as subspace LDA, stepwise LDA [35], [36], etc.”
Locality-Preserving Dimensionality Reduction and Classification for Hyperspectral Image Analysis (2012, background): “The resulting algorithms are thus referred to as LDA-MLE and RLDA-MLE in this paper.”
Optimal Clustering Framework for Hyperspectral Band Selection (2018, positive): “They are k-nearest neighborhood (KNN) [46], linear discriminant analysis (LDA) [47], support vector machine (SVM) [48], and edge-preserving filtering (EPF) [49].”
Multiple Feature Learning for Hyperspectral Image Classification (2015, methodology): “For instance, in linear discriminant analysis [15], a linear function is used in order to maximize the discriminatory power and separate the available classes effectively.”
Local Binary Patterns and Extreme Learning Machine for Hyperspectral Imagery Classification (2015, background): “In conventional HSI classification systems, classifiers [1]–[4] solely consider spectral signatures while ignoring spatial information at neighboring locations.”
Deep Feature Extraction and Classification of Hyperspectral Images Based on Convolutional Neural Networks (2016, methodology): “In the early stage of the study on HSI FE, the focus was on spectral-based methods, including principal component analysis (PCA) [5], independent component analysis (ICA) [6], linear discriminant analysis [7], etc.”
Hyperspectral Image Classification With Deep Learning Models (2018, methodology): “[15] utilize a linear discriminant method to solve the problem.”