Kronecker Factorization-Based Multinomial Logistic Regression for Hyperspectral Image Classification

Abstract

Multinomial logistic regression (MLR) has become prevailing for supervised learning within hyperspectral images (HSIs) community. It seeks the optimal regressors with the given training data. To better understand HSI data and learn more representative spatial feature, in this letter, a unified framework which combines MLR classifier training and Kronecker factorization (KF)-based feature learning for joint optimization is proposed. It is called Kronecker factorization-based multinomial logistic regression algorithm (KF-MLR). Besides, with Gabor wavelet transform to feed input, two data-oriented strategies are tailored. One is to reshape the feature learning part as a bilinear form instead such that the structure information among different wavelet filters can be exploited as much as possible. The other is to add local regularization term to preserve discriminant information as well. The regressors and feature factor matrices are optimized in iterative fashion. KF-MLR is investigated on several popular HSI datasets. The random experimental results prove it a competitive and promising classifier when compared with other state-of-the-art techniques.