Analysing Effect of t-SNE and 1-D CNN on Performance of Hyperspectral Image Classification

Abstract

Feature extraction is a crucial step in Hyperspectral Image classification that aids in processing data effectively without losing relevant information. This step is essential when dealing with images with high dimensions because they suffer from Hughes phenomenon or the curse of high dimensionality. This phenomenon occurs in high dimensional datasets where the number of training samples is limited. In this paper, we have studied the influence of feature extraction techniques in HSI classification. We have compared the efficiency of three widely used techniques, namely Principal Component Analysis, t- Stochastic Neighbor Embedding and Convolutional Neural Network. Overall classification accuracy for PCA when used with KNN, a commonly used classification algorithm was found to be 69.79% while t-SNE with KNN was 71.04%. When CNN was used for feature extraction, its outperformed t-SNE and PCA with a wide margin with classification accuracy reaching as high as 95.06%.