Hyperspectral Image Analysis using Principal Component Analysis and Siamese Network

Abstract

The process of collecting information in hundreds of images having bands with different wavelengths using remotely sensed devices is called Hyperspectral Imaging Technology. So many methods are used for analysis of this hyperspectral information of which Classification is a popular one, which is the process of assigning label to each pixel. For classification of hyperspectral data, many supervised methods are proposed in literature with excellent performance, but the quality the classifier depends strongly on the number of training samples used to construct the model. Data Augmentation (DA) is a strategy that can increase the quantity of training data and effective to overcome the limited training samples problem. The hyperspectral data set suffers with curse of dimensionality with redundant information, influencing the classification accuracy. In this paper, a supervised model for hyperspectral data set is proposed, with dimensionality reduction using Principal Component Analysis and data augmentation using mixed pixels is applied to increase training samples and tested using Siamese network and finally classification is done using CNN.