In this paper, we propose an unsupervised method for hyperspectral remote sensing image segmentation. The method exploits the mean-shift clustering algorithm that takes as input a preliminary hyperspectral superpixels segmentation together with the spectral pixel information. The proposed method does not require the number of segmentation classes as input parameter, and it does not exploit any a-priori knowledge about the type of land-cover or land-use to be segmented (e.g. water, vegetation, building etc.). Experiments on Salinas, SalinasA, Pavia Center and Pavia University datasets are carried out. Performance are measured in terms of normalized mutual information, adjusted Rand index and F1-score. Results demonstrate the validity of the proposed method in comparison with the state of the art.

翻译：在本文中,我们提出了一个超光谱遥感图象分离的不受监督的方法。该方法利用了将初步超光谱超像素分离和光谱像素信息作为投入的中位组合算法。拟议方法不要求将分解类别的数目作为输入参数,也不利用关于土地覆盖或土地使用类型的优先知识(如水、植被、建筑等)进行分解。对萨利纳斯、萨利纳萨、帕维亚中心和帕维亚大学数据集进行了实验。用正常的相互信息、经调整的Rand指数和F1核心来衡量了绩效。结果表明,与艺术现状相比,拟议方法的有效性。