Randomized Principal Component Analysis for Hyperspectral Image Classification
This work addresses computational complexity in hyperspectral image processing for remote sensing applications, but it is incremental as it compares existing methods without introducing new techniques.
The paper tackled the challenge of high-dimensional hyperspectral image classification by comparing PCA and randomized PCA for dimensionality reduction, finding that PCA outperformed R-PCA with SVM but yielded similar accuracies with LightGBM, achieving up to 99.25% accuracy on the Pavia University dataset.
The high-dimensional feature space of the hyperspectral imagery poses major challenges to the processing and analysis of the hyperspectral data sets. In such a case, dimensionality reduction is necessary to decrease the computational complexity. The random projections open up new ways of dimensionality reduction, especially for large data sets. In this paper, the principal component analysis (PCA) and randomized principal component analysis (R-PCA) for the classification of hyperspectral images using support vector machines (SVM) and light gradient boosting machines (LightGBM) have been investigated. In this experimental research, the number of features was reduced to 20 and 30 for classification of two hyperspectral datasets (Indian Pines and Pavia University). The experimental results demonstrated that PCA outperformed R-PCA for SVM for both datasets, but received close accuracy values for LightGBM. The highest classification accuracies were obtained as 0.9925 and 0.9639 by LightGBM with original features for the Pavia University and Indian Pines, respectively.