Semisupervised Classification Based on SLIC Segmentation for Hyperspectral Image

Abstract

With the high spectral resolution, hyperspectral image (HSI) can provide a wealth of information for image classification. Many classification methods utilize the training samples to classify the ground materials. However, the small sample problem is still urgent to be solved when considering the cost of labeling training samples. In order to solve this problem, this letter proposes a semisupervised classification method based on the simple linear iterative cluster (SLIC) segmentation for HSI. This method improves the SLIC method to better explore the spectral characteristic of HSI. It explores the learned superpixel map and initial classification map to select the pseudo-labeled samples (PLSs), which is expected to increase the effectiveness of PLSs. The final classification map can be obtained with the integrated labeled training samples and PLSs. Experiments were carried out on three HSIs, and it was founded that the proposed method generally shows a better classification performance than the other methods.