Hyperspectral Image Classification With Independent Component Discriminant Analysis

By Alberto Villa, Jon Atli Benediktsson, Jocelyn Chanussot, Christian Jutten. IEEE Transactions on Geoscience and Remote Sensing, 2011

406 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Biomedical Engineering, Media Technology

View Paper: https://www.semanticscholar.org/paper/e2769447c40c94e0ec7f3ac46ee6d612edd4dbd9

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Analytical Chemistry

1. Pedram Ghamisi
2. Antonio J. Plaza
3. Shutao Li
4. Javier Plaza
5. Jon Atli Benediktsson
6. Yushi Chen
7. Xudong Kang
8. Leyuan Fang

In Biomedical Engineering

1. Xudong Kang
2. Shutao Li
3. Pedram Ghamisi
4. Leyuan Fang
5. Jon Atli Benediktsson
6. Xuanlin Xiang
7. Zebin Wu
8. Antonio J. Plaza

In Media Technology

1. Shutao Li
2. Antonio J. Plaza
3. Pedram Ghamisi
4. Jon Atli Benediktsson
5. Leyuan Fang
6. Yushi Chen
7. Javier Plaza
8. Qian Du

Citations in the corpus

• 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. [8], [9].”
• PCA-Based Edge-Preserving Features for Hyperspectral Image Classification (2017, methodology): “As shown in Table II, compared with other widely used dimension-reduction methods, i.e., the linear discriminant analysis [53], ICA [54], probabilistic PCA [55], and kernel PCA (KPCA) [56] methods, the adopted PCA method is simple, efficient, and, more importantly, able to obtain the best classifica”
• Deep Few-Shot Learning for Hyperspectral Image Classification (2019, methodology): “Simple feature extraction methods use principal component analysis [7], independent component analysis [8], or local linear feature extraction [9] for dimensionality reduction.”
• Deep Learning for Hyperspectral Image Classification: An Overview (2019, background): “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].”
• 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].”
• Local Binary Patterns and Extreme Learning Machine for Hyperspectral Imagery Classification (2015, methodology): “The standardized McNemar’s test [2] is employed to verify”
• Deep Pyramidal Residual Networks for Spectral–Spatial Hyperspectral Image Classification (2019, methodology): “In these models, the HSI is normally preprocessed via PCA or similar dimension reduction methods (such as independent component analysis [59] or maximum noise fraction [60], and among others) in order to reduce the number of spectral bands, and neighboring regions pi, j ∈ Rd×d are extracted from the”