Semisupervised Hyperspectral Image Classification via Discriminant Analysis and Robust Regression

In recent years, hyperspectral image classification (HSIC) has received increasing attention in a wide range of hyperspectral applications. It is still very challenging due to the following factors: 1) there are not enough labeled samples; 2) the images are easy to be polluted by outlier channels; and 3) different objects may have similar spectra. Considering these three factors, we propose a novel semisupervised HSIC method, which is constructed on discriminant analysis and robust regression (DARR). Specifically, a regression-based semisupervised technique is employed by not only exploiting the rich information in labeled samples, but also taking advantage of abundant unlabeled ones. In this way, the true data distribution can be obtained accurately. Then, we introduce a robust adaptive loss function to measure the representation loss. As a result, it can greatly relieve the side effects of outlier channels. Finally, to increase discriminating power of our approach for different objects, we utilize the pairwise constraints to incorporate the discriminant information among labeled samples. Through these constraints, the same-category samples are projected to be close to each other, while the different-category samples are as far apart as possible. The above three components can be integrated into a graph-based objective function, whose optimization is systematically provided. Extensive experiments on four data sets demonstrate that our method achieves higher quantitative results, as well as more satisfactory visual performances by comparing with state-of-the-art methods and using different parameter settings.