发表机构
Catholic University of Pelotas; Federal University of Santa Catarina（天主教佩洛塔斯大学; 圣卡塔琳娜联邦大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种包含专门处理像素相对空间位置的第三种核的复合核方法，用于基于图的半监督高光谱图像分类，实验证明该方法在真实图像上提升了分类精度。
AI 中文摘要
高光谱图像（HIs）的分类仍然面临若干挑战，其中之一是难以获取大量标记样本来训练分类器。半监督学习方法近年来受到广泛关注，因为它们仅需对少量图像像素进行初始标记，并在实际应用中取得了非常好的效果。基于图的半监督高光谱图像分类中的一个开放问题是需要考虑图像中像素之间的相对空间关系，以提高解的平滑性。使用复合核的基于核的方法已经取得了非常好的结果，其中一种核处理像素的光谱特性，而另一种核处理某些空间特性。大多数现有解决方案采用光谱-空间核，该核考虑每个像素周围空间区域的光谱特性。本工作提出了一种复合核方法，该方法包含第三种核，专门处理像素的相对空间位置。在真实高光谱图像上的实验表明，与文献中先前报道的结果相比，使用新的空间核带来了改进的分类结果。这些结果也揭示了光谱核和光谱-空间核在半监督高光谱图像分类中的相对贡献。
英文摘要
The classification of hyperspectral images (HIs) still presents several challenges. One of them is the difficulty to obtain a large set of labeled samples to train the classifier. Semi-supervised learning methods have received much attention recently, as they require the initial labeling of a reduced number of image pixels and lead to very good results for practical application. One of the open problems in graph-based semi-supervised HI classification is the need to consider relative spatial relationship between pixels in the image to improve the smoothness of the solution. Kernel-based approaches using composite kernels have led to very good results, in which one kernel addresses the spectral properties of the pixels while a second kernel addresses some spatial properties. Most available solutions employ a spectral-spatial kernel which considers the spectral properties of a spatial region about each pixel. This work proposes a composite kernel approach that includes a third kernel dealing exclusively with the relative spatial position of the pixels. Experiments with real HI images show that the use of the new spatial kernel has led to improved classification results when compared to those previously reported in the literature. These results also shed some light on the relative contributions of the spectral and spectral-spatial kernels in semi-supervised HI classification.
Comments32 pages, 5 figures. Research work
