Aiguang Li

The first law of geography is one of the most important concepts in geographical analysis, revealing the significant role of spatial proximity. At present, some current international relation studies or geographic network analysis studies tend to build corresponding network models according to different themes, but the most basic level of geographic neighborhoods is intentionally or unintentionally neglected in those processes. Based on the adjacency relationship between the terrestrial countries in the world, the model of the terrestrial adjacency network (TAN) is constructed. The model includes almost all land-based countries and is divided into three main regions, respectively, Eurasia, Africa, and America. On the mathematical model of these regions, we analyze the geospatial structure and network evolution of the adjacent networks utilizing statistical methods and network analysis methods. This study helps to map and understand the geographical attributes and characteristics of countries from the perspective of holistic structure, aiming to provide a quantitative reference for subsequent research on international relations and geographic computing. Moreover, despite some limitations, TAN represents a new advance in geographical network analysis that can be further applied by overlaying more attribute data.

Convolutional neural networks (CNNs) and graph convolutional networks (GCNs) have led to promising advancements in hyperspectral image (HSI) classification; however, traditional CNNs with fixed square convolution kernels are insufficiently flexible to handle irregular structures. Similarly, GCNs that employ superpixel nodes instead of pixel nodes may overlook pixel-level features; both networks tend to extract features locally and cause loss of multilayer contextual semantic information during feature extraction due to the fixed kernel. To leverage the strengths of CNNs and GCNs, we propose a multiscale pixel-level and superpixel-level (MPAS)-based HSI classification method. The network consists of two sub-networks for extracting multi-level information of HSIs: a multi-scale hybrid spectral–spatial attention convolution branch (HSSAC) and a parallel multi-hop graph convolution branch (MGCN). HSSAC comprehensively captures pixel-level features with different kernel sizes through parallel multi-scale convolution and cross-path fusion to reduce the semantic information loss caused by fixed convolution kernels during feature extraction and learns adjustable weights from the adaptive spectral–spatial attention module (SSAM) to capture pixel-level feature correlations with less computation. MGCN can systematically aggregate multi-hop contextual information to better model HSIs’ spatial background structure using the relationship between parallel multi-hop graph transformation nodes. The proposed MPAS effectively captures multi-layer contextual semantic features by leveraging pixel-level and superpixel-level spectral–spatial information, which improves the performance of the HSI classification task while ensuring computational efficiency. Extensive evaluation experiments on three real-world HSI datasets demonstrate that MPAS outperforms other state-of-the-art networks, demonstrating its superior feature learning capabilities.