Despite much advance obtained in hyperspectral image sensors, they are still very sensitive to the noise, and thus cause the captured data to carry enough noise to degrade the classification results. The traditional approach first resorts to image denoising and then feeds the denoised image into a classifier. However, such a straightforward approach, treating denoising and classification separately, suffers greatly from neglecting their impacts on each other. This paper presents a new simultaneous denoising and classification method in the pursuit of cleanest image for optimal classification in the sense of given task evaluation measures. To obtain this objective, we develop a hybrid conditional random field (CRF) (for denoising) and multinomial logistic regression (MLR) (for classification) model at first, and then to train the proposed hybrid model, we propose a new joint learning method, which can effectively capture the impacts of denoising on classification, or vice versa, the effects of classification on denoising. Through the proposed joint learning method, the CRF and MLR, and thus the denoising and classification procedure, can be tightly combined. Moreover, the proposed joint learning method can directly optimize a large class of application specific performance measures including both the linear measures, such as the overall accuracy, and the nonlinear measures, such as kappa statistics. Meanwhile, the consistency between the criteria of model learning and model application has the potential to obtain the denoised image, which is at its best for optimal classification in the sense of the given measure. The extensive experiments of simultaneous denoising and classification tasks are conducted in both simulated and real noisy conditions to test our jointly learned model, which are shown to outperform the conventional methods of treating the two tasks independently.
Zhong et al. (Fri,) studied this question.