Locality-Preserving Dimensionality Reduction and Classification for Hyperspectral Image Analysis

By Wei Li, Saurabh Prasad, James E. Fowler, Lori Mann Bruce. IEEE Transactions on Geoscience and Remote Sensing, 2012

457 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/f5f9dbf8b8537445adc4824d0a427c0138fba457

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In Analytical Chemistry

In Computer Vision and Pattern Recognition

In Media Technology

Citations in the corpus

Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art (2017, methodology): “[48] employed local Fisher’s LDA [49] to reduce the dimensionality of the data while preserving the corresponding multimodal structure.”
Local Binary Patterns and Extreme Learning Machine for Hyperspectral Imagery Classification (2015, methodology): “SVM [3], [5] has been proved to be powerful in HSI classification.”
Multisource Remote Sensing Data Classification Based on Convolutional Neural Network (2018, methodology): “The SVM [29], [30], with a nonlinear kernel 0196-2892 © 2017 IEEE.”
Spectral–Spatial Hyperspectral Image Classification via Multiscale Adaptive Sparse Representation (2014, background): “, local Fisher’s discriminant [21] and extended morphological profiles (EMPs) [22]).”
A Novel Ranking-Based Clustering Approach for Hyperspectral Band Selection (2016, methodology): “separability measure of training samples and their class labels [20], [21], whereas the importance of a band obtained by unsupervised methods is evaluated by various statistical measures”
Generalized Composite Kernel Framework for Hyperspectral Image Classification (2013, background): “The good generalization capability of machine learning techniques such as the support vector machine (SVM) [6] and multinomial logistic regression (MLR) [8]–[12] can still be enhanced by reducing the input data dimensionality [13], [14], which can help in addressing ill-posed problems based on limit”
Manifold-Learning-Based Feature Extraction for Classification of Hyperspectral Data: A Review of Advances in Manifold Learning (2014, unknown): “The potential value of manifold learning for HSI analysis has been demonstrated for applications including feature extraction [1], [11], segmentation [12], classification [13]–[15], anomaly detection [16], [17], and spectral unmixing [18]–[21] with some approaches exploiting interband correlation [1”