Feature Extraction & Classification of Hyperspectral Images using Singular Spectrum Analysis & Multinomial Logistic Regression Classifiers

This paper presents a new approach for hyperspectral feature extraction & image classification exploiting spectral spatial information of HSI dataset. Various methods have been developed to improve the classification accuracy. SSA has been applied to the spectral profile of the pixel where the 1-D signal can be composed into the sum of independent components including noise. Removing noisy component in extracting features, it results in the improvement of classification accuracies. Experiment results show that this SSA approach improved results in classification process using multinomial logistic regression classifiers.