Principal Component Analysis for Hyperspectral Image Classification

By C. Rodarmel, Jie Shan. 2002

541 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/c9eb2d77f24673dde1d5105feb6c2d75915d682c

How researchers see this paper

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

1. Xuchu Yu
2. Gang Wan
3. Ruirui Wang
4. Bing Liu
5. Pengqiang Zhang
6. Anzhu Yu
7. Nassim Ait Ali Braham

Citations in the corpus

• Deep Learning for Classification of Hyperspectral Data: A Comparative Review (2019, unknown): “Among the various preprocessing methods for feature generation that have been used, we can cite feature extraction algorithms, such as the well-known PCA [20] or, more recently, random feature selection [21].”
• 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.”