A semisupervised deformed kernel function, using low-rank representation with consideration of local geometrical structure of data, is presented for the classification of hyperspectral images. The proposed method incorporates the wealth of unlabeled information to deal with the limited labeled samples situation as a common case in HSIs applications. The proposed kernel needs to be computed before training the classifier, e.g., a support vector machine, and it relies on combining the standard radial basis function kernel based on labeled information and the low-rank representation kernel obtained using all available (labeled and unlabeled) information. The low-rank representation kernel can overcome the difficulties of clustering methods that are used to construct the kernels such as bagged kernel and multi-scale bagged kernel. The experimental results of two well-known HSIs data sets demonstrate the effectiveness of the proposed method in comparison with cluster kernels obtained using traditional clustering methods and graph learning methods.

中文翻译：

---

具有低秩表示核的高光谱图像的半监督分类。

提出了一种使用低秩表示并考虑数据的局部几何结构的半监督变形核函数，用于高光谱图像的分类。所提出的方法结合了大量未标记的信息，以处理有限的标记样本情况，这是HSI应用中的常见情况。建议的内核需要在训练分类器（例如支持向量机）之前进行计算，并且它依赖于结合基于标记信息的标准径向基函数内核和使用所有可用（标记和未标记）获得的低秩表示内核信息。低秩表示内核可以克服用于构建内核的聚类方法（例如袋装内核和多尺度袋装内核）的困难。