Combining Morphological Attribute Profiles via an Ensemble Method for Hyperspectral Image Classification
Morphological attribute profiles (APs) are discriminant features in the spectral-spatial classification of hyperspectral data. However, the optimal range of parameters in each filter is always a challenging yet important task, since an unsuitable range of parameters likely leads to inferior results. In order to alleviate this problem, we propose an ensemble method, which integrates multiple classification results based on a series of APs. The APs are obtained by using different filters with thresholds that are randomly selected from an arbitrarily defined range of parameters. Experimental results conducted on two hyperspectral images demonstrate the robustness and effectiveness of the proposed method.