Spatial Preprocessing Based Multinomial Logistic Regression for Hyperspectral Image Classification

Abstract The paper presents a fast, reliable and efficient method for improving hyperspectral image classification aided by segmentation. The Multinomial Logistic Regression(MLR) algorithm can be extended to a semi-supervised learning of the posterior class distribution using unlabeled samples actively selected from the dataset. Classification results obtained from regression model is improved by performing a maximum a posteriori segmentation as it considers the spatial information of the hyperspectral image. The addition of the spatial processing step prior to the above mentioned classification scheme improves the overall accuracy of the process. The accuracies obtained before and after applying the preprocessing are compared.