基于空间约束的谱聚类区域划分算法
Spatially Constrained Spectral Clustering Algorithms for Region Delineation
摘要：
区域化是将景观划分为具有相似属性的均匀斑块的任务。虽然这项任务有广泛的应用，但它有两个显著的挑战。首先，假设所得到的区域是均匀的和空间连续的。第二，众所周知，景观是有层次的，因此，精细尺度的区域完全嵌套在更广泛的区域内。为了解决这两个挑战，首先，我们开发了一个空间约束的谱聚类框架，区域划分，结合区域同质性和空间连续性之间的权衡。该框架使用灵活的，截断指数核来表示空间邻接约束，这是集成的景观特征相似性矩阵的区域划定。为了解决第二个挑战，我们扩展了框架，以创建嵌套在更广泛的规模的区域使用贪婪，递归二分法的精细规模的区域。我们提出了一个案例研究的陆地生态数据集在美国，比较拟议的框架与几个基线方法进行区域化。实验结果表明，所提出的区域化框架优于基线方法，特别是在平衡区域连续性和均匀性方面，以及创建更相似大小的区域，这通常是区域的期望特征。
英文摘要：
Regionalization is the task of dividing up a landscape into homogeneous patches with similar properties. Although this task has a wide range of applications, it has two notable challenges. First, it is assumed that the resulting regions are both homogeneous and spatially contiguous. Second, it is well-recognized that landscapes are hierarchical such that fine-scale regions are nested wholly within broader-scale regions. To address these two challenges, first, we develop a spatially constrained spectral clustering framework for region delineation that incorporates the tradeoff between region homogeneity and spatial contiguity. The framework uses a flexible, truncated exponential kernel to represent the spatial contiguity constraints, which is integrated with the landscape feature similarity matrix for region delineation. To address the second challenge, we extend the framework to create fine-scale regions that are nested within broader-scaled regions using a greedy, recursive bisection approach. We present a case study of a terrestrial ecology data set in the United States that compares the proposed framework with several baseline methods for regionalization. Experimental results suggest that the proposed framework for regionalization outperforms the baseline methods, especially in terms of balancing region contiguity and homogeneity, as well as creating regions of more similar size, which is often a desired trait of regions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
