The Utilization Of Multi-Label Samples For Hyperspectral Image Classification

The number and quality of training samples have a big influence on hyperspectral image classification performance. However, it is often difficult to manually annotate a large number of accurate training samples because the annotation requires a lot of manpower and resources. In this paper, we first propose a multi-labeling method to label the training samples efficiently. Instead of giving the exact label for each training pixel, we just precisely label a small number of pixels (called single-label samples), and annotate a large number of pixels in certain regions together (called multi-label samples) with multiple labels. Furthermore, a superpixel segmentation and recursive filtering based sample enhancing method is proposed to make full use of multi-label training samples for classification, which consists of the following major steps: IFRF based feature extraction, superpixels based classification, and spatial-spectral similarity based inaccurate samples removal. Experimental results demonstrate that the proposed method can improve the classification accuracy of multiple classifiers with multi-label training samples.