Longwave infrared hyperspectral image classification via an ensemble method

Longwave infrared hyperspectral images (LWIR-HSIs) classification is challenging, due to the poor imaging quality and low signal-to-noise ratio. A popular viewpoint is that abundant spatial contextual information can significantly improve the classification accuracies. However, it is quite difficult to determine what degree of spatial information is the most useful. In this article, we develop a novel ensemble-based classification method, which is able to fully leverage joint spectral-spatial features in different degrees. The proposed method contains three primary steps. First, a powerful edge-preserving filtering (EPF) approach, rolling guidance filtering (RGF), is utilized to generate several groups of diverse samples as well as enhance the quality of the LWIR-HSI data. Each group corresponds to a certain degree of spatial information. Subsequently, a series of individual classifiers are learned based on all groups of training samples, and each classifier could provide a single classification result for all test samples. Finally, we propose a new ensemble strategy, multi-classifier k-statistic (MKS), to evaluate the contributions of individual learners (ILs). The final classification results are obtained based on the outputs of RGF and MKS. Experiments on a challenging LWIR-HSI data set verify the effectiveness of the proposed method, compared with some state-of-the-art HSI classification methods.