论文标题
用于聚类的动态系统算法在高光谱图像中
A Dynamical Systems Algorithm for Clustering in Hyperspectral Imagery
论文作者
论文摘要
在本文中，我们提出了一种用于在高光谱图像中聚类的新动力学系统算法。该算法的主要思想是，数据点是在增加密度和最终位于同一密集区域的像素组的方向上推动的。这本质上是由数据歧管上数据点密度梯度定义的微分方程的数值解。类的数量是自动化的，所得的聚类可能非常准确。除了提供准确的聚类外，该算法还提出了一种新的工具，可以理解高维度的高光谱数据。我们在Urban上评估了算法（可在www.tec.ary.mil/hypercube/上获得）场景，将性能与K-Means算法进行比较，使用预识别的材料类别作为地面真理。
In this paper we present a new dynamical systems algorithm for clustering in hyperspectral images. The main idea of the algorithm is that data points are pushed\' in the direction of increasing density and groups of pixels that end up in the same dense regions belong to the same class. This is essentially a numerical solution of the differential equation defined by the gradient of the density of data points on the data manifold. The number of classes is automated and the resulting clustering can be extremely accurate. In addition to providing a accurate clustering, this algorithm presents a new tool for understanding hyperspectral data in high dimensions. We evaluate the algorithm on the Urban (Available at www.tec.ary.mil/Hypercube/) scene comparing performance against the k-means algorithm using pre-identified classes of materials as ground truth.
