Journal of Computer Applications (计算机应用) 2013/33:12 PP.3441-3443
Most supervised classification methods require large training samples to avoid the well-known Hughes effect. However, labeling samples is often very expensive in actual world applications. In order to reduce the number of training samples, high-quality training samples are extremely important. A hyperspectral image classification based on active learning was proposed. It provided a new calculation method for concerning region attention degree to combine spectral and spatial characteristics of the image effectively, and used active learning method to obtain training set with the most abundant information and improved the classification accuracy ultimately. The experimental results show that the proposed method performs particularly well for the classification of hyperspectral images, when compared to random sampling supervised classification method and active learning approaches.