Impact of Dimension Reduced Spectral Features on Open Set Domain Adaptation for Hyperspectral Image Classification

Abstract

Hyperspectral image classification has so many applications in the area of remote sensing. In recent years, deep learning has been accepted as a powerful tool for feature extraction and ensuring better classification accuracies. In this paper, model for HSI classification is created by implementing open set domain adaptation and generative adversarial networks (GAN). Open set domain adaptation is a type of domain adaptation where target has more classes which are not present in the source distribution. Huge dimension of hyperspectral image needs to be reduced for an efficient classification. In this work, we analysed the effect of dimensionality reduction for open set domain adaptation for hyperspectral image classification by using dynamic mode decomposition (DMD) technique. Experimental results show that 20% of the total available bands of Salinas and 30% of the bands of PaviaU dataset are the highest achievable reduction in feature dimension that results in almost same classification accuracy.