分类栅格数据的类别编码值仅代表语义类型,不具备数值含义,导致当前空间信息度量算法难以应用于分类栅格数据。针对此问题,本文从特征相似性与多样性视角出发,提出基于像元邻域边界方形度的分类栅格数据空间信息度量方法。该方法通过计算像元邻域边界与方形之间的相似性程度,定量度量分类栅格数据中同质像元所表达的空间形态信息；通过邻域内异质像元的多样性概率熵定量度量分类栅格数据中异质像元所表达的空间结构信息；以邻域内异质像元的空间占位所能实现的最大多样性熵值作为权重,综合计算分类栅格数据的空间信息量。最后,从类别编码值的变化、热力学一致性及破碎度影响三方面,开展了多组对比实验与应用示范。结果显示论文方法的度量结果不受类别编码值变化的影响,与热力学一致性和栅格数据破碎度呈现强相关关系,相关系数均大于0.98。实验结果与应用示例表明该方法能够有效度量分类栅格数据的空间信息。

The category encoding values of classified grid data solely represent semantic types without numerical significance, posing challenges for the application of current spatial information measurement algorithms to classified grid data. To address this issue, this paper proposes a spatial information measurement method for classified grid data based on the squareness of pixel neighborhood boundaries, from the perspectives of feature similarity and diversity. This method quantitatively measures the spatial morphological information expressed by homogeneous pixels in classified grid data by calculating the degree of similarity between pixel neighborhood boundaries and squares. It also quantitatively measures the spatial structural information expressed by heterogeneous pixels in classified grid data through the diversity probability entropy of heterogeneous pixels within the neighborhood. The spatial information content of the classified grid data is comprehensively calculated, with the spatial occupation of heterogeneous pixels within the neighborhood serving as weights. Finally, multiple sets of comparative experiments and application demonstrations are conducted from three aspects: changes in category encoding values, thermodynamic consistency, and the impact of fragmentation. The results show that the measurement outcomes of the proposed method are not affected by changes in category encoding values and exhibit a strong correlation with thermodynamic consistency and the fragmentation of grid data, with correlation coefficients all greater than 0.98. The experimental results and application examples demonstrate that this method can effectively measure the spatial information of classified grid data.