Robust Hyperspectral Classification Using Relevance Vector Machine
Robust Hyperspectral Classification Using Relevance Vector Machine
复制标题
使用相关向量机的鲁棒高光谱分类
DOI：
10.1109/tgrs.2010.2103381
复制
发表时间：
2011-06-01
影响因子：
8.2
通讯作者：
Zhang, Ye
中科院分区：
文献类型：
--
作者：
Mianji, Fereidoun A.;Zhang, Ye
维数灾难是监督高光谱分类中计算复杂性和休斯现象的主要原因。以往的研究很少同时考虑可用训练样本不足的真实情况，特别是对于往往包含场景关键信息的小土地覆盖，以及复杂性问题。在本文中，将用于区分的特征缩减技术的功能与基于贝叶斯学习的概率稀疏核模型（相关向量机（RVM））的优点相结合，开发了一种新的监督分类方法。在所提出的方法中，首先使用特征约简技术将超维数据变换到较低维特征空间，以最大化类之间的可分离性。然后，转换后的数据由基于并行架构和一对一策略的多类 RVM 分类器进行处理。为了验证该方法的有效性，在真实的高光谱数据上进行了实验。将结果与最有效的监督分类技术（例如使用适当性能指标的支持向量机）进行比较。结果表明，所提出的方法比其他方法表现更好，特别是对于难以精确分类的小而分散的土地覆盖类别。此外，该方法还具有计算复杂度低、对休斯现象具有鲁棒性等优点。
The curse of dimensionality is the main reason for the computational complexity and the Hughes phenomenon in supervised hyperspectral classification. Previous studies seldom consider in a simultaneous fashion the real situation of insufficiency of available training samples, particularly for small land covers that often contain the key information of the scene, and the problem of complexity. In this paper, the capabilities of a feature reduction technique used for discrimination are combined with the advantages of a Bayesian learning-based probabilistic sparse kernel model, the relevance vector machine (RVM), to develop a new supervised classification method. In the proposed method, the hyperdimensional data are first transformed to a lower dimensionality feature space using the feature reduction technique to maximize separability between classes. The transformed data are then processed by a multiclass RVM classifier based on the parallel architecture and one-against-one strategy. To verify the effectiveness of the method, experiments were carried out on real hyperspectral data. The results are compared with the most efficient supervised classification techniques such as the support vector machine using appropriate performance indicators. The results show that the proposed method performs better than the other approaches particularly for small and scattered landcover classes which are harder to be precisely classified. In addition, this method has the advantages of low computational complexity and robustness to the Hughes phenomenon.