The cluster patterns of features in map space represent a comprehensive reflection of individual feature geometric attributes and their spatial adjacency relationships. These patterns also embody spatial cognition results under the Gestalt principle. Describing non-linear spatial cluster patterns as effective regular structures is one of the fundamental tasks in deep learning for recognizing feature cluster patterns. In this study, based on the concept of texture co-occurrence matrices from regular gray-scale images, we utilized Voronoi diagrams to construct the tessellation structure of building polygons. Built upon the foundation of first-order texton co-occurrence matrices, we established three-dimensional texton co-occurrence matrices for building polygons, considered five attributes of building size, shape, orientation, and density, and encompassed 64 different combinations of second-order neighboring directions. This matrix concretizes the latent Gestalt spatial characteristics of building polygon clusters into a three-dimensional sparse matrix. It is then used as an input vector to construct a deep convolutional neural network for recognizing building polygon cluster patterns. Through adjustments and optimizations of neural network structure and strategies, along with validation through practical case studies and comparisons with other models, we have demonstrated the effectiveness of the second-order texton co-occurrence matrix in describing the characteristics of building polygon clusters.

中文翻译：

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使用CNN网络构建多边形簇的二阶纹理特征提取和模式识别

地图空间中地物的聚类模式是个体地物几何属性及其空间邻接关系的综合反映。这些模式也体现了格式塔原理下的空间认知结果。将非线性空间聚类模式描述为有效的规则结构是深度学习中识别特征聚类模式的基本任务之一。在本研究中，基于规则灰度图像的纹理共生矩阵的概念，我们利用Voronoi图来构造建筑物多边形的镶嵌结构。在一阶纹理共生矩阵的基础上，我们建立了用于构建多边形的三维纹理共生矩阵，考虑了建筑物尺寸、形状、方向和密度五个属性，并包含了 64 种不同的二阶纹理组合。订购相邻方向。该矩阵将构建多边形簇的潜在格式塔空间特征具体化为三维稀疏矩阵。然后将其用作输入向量来构建深度卷积神经网络，以识别构建多边形簇模式。通过对神经网络结构和策略的调整和优化，结合实际案例的验证以及与其他模型的比较，我们证明了二阶纹理共生矩阵在描述构建多边形簇特征方面的有效性。