SWT-PCA-CNN: hyperspectral image classification with multi-stage feature extraction and parameter tuning

Abstract

Hyperspectral imaging is an increasingly popular technique in remote sensing, offering a wealth of spectral information for a range of applications. This paper presents a comparative study of hyperspectral image classification techniques using three different datasets: Indian Pines, Salinas, and Pavia University. The study employs a combination of three methods, namely stationary wavelet transforms (SWT), principal component analysis (PCA), and convolutional neural network (CNN), to develop a model for hyperspectral image classification. The proposed approach combines SWT and PCA for spatial feature extraction and dimensionality reduction, followed by classification using CNN. Furthermore, the study performs parameter tuning by changing the optimizer, activation function, and filter size of the CNN model on the Indian Pines dataset. The results demonstrate that the proposed SWT-PCA-CNN approach outperforms the conventional DWT-PCA and PCA-KNN algorithms, achieving an overall classification accuracy of 98.2%, 99.86%, 99.80% on the Indian Pines, Salinas and Pavia University datasets respectively. The study highlights the effectiveness of the proposed approaches for hyperspectral image classification and their potential for applications in remote sensing and other fields.