A Graph Based Classification Method for Hyperspectral Images

The goal of this paper is to apply graph cut (GC) theory to the classification of hyperspectral remote sensing images. The task is formulated as a labeling problem on Markov Random Field (MRF) constructed on the image grid, and graph cut algorithm is employed to solve this task. In general, a large number of user interactive strikes are necessary to obtain satisfactory segmentation results. Due to the spatial variability of spectral signatures, however, hyperspectral remote sensing images often contain many tiny regions. Labeling all these tiny regions usually needs expensive human labor. To overcome this difficulty, a pixel-wise fuzzy classification based on support vector machine (SVM) is first applied. As a result, only pixels with high probabilities are preserved as labeled ones. This generates a pseudo user strike map. This map is then employed for graph cut to evaluate the truthful likelihoods of class labels and propagate them to the MRF. To evaluate the robustness of our method, we have tested our method on small training sets. Additionally, comparisons are made between the results of SVM, SVM with stacking neighboring vectors, SVM with morphological pre-processing and our method. Comparative experimental results demonstrate the validity of our method.