Satellite missions and Earth Observation (EO) systems represent fundamental assets for environmental monitoring and the timely identification of catastrophic events, long-term monitoring of both natural resources and human-made assets, such as vegetation, water bodies, forests as well as buildings. Different EO missions enables the collection of information on several spectral bandwidths, such as MODIS, Sentinel-1 and Sentinel-2. Thus, given the recent advances of machine learning, computer vision and the availability of labeled data, researchers demonstrated the feasibility and the precision of land-use monitoring systems and remote sensing image classification through the use of deep neural networks. Such systems may help domain experts and governments in constant environmental monitoring, enabling timely intervention in case of catastrophic events (e.g., forest wildfire in a remote area). Despite the recent advances in the field of computer vision, many works limit their analysis on Convolutional Neural Networks (CNNs) and, more recently, to vision transformers (ViTs). Given the recent successes of Graph Neural Networks (GNNs) on non-graph data, such as time-series and images, we investigate the performances of a recent Vision GNN architecture (ViG) applied to the task of land cover classification. The experimental results show that ViG achieves state-of-the-art performances in multiclass and multilabel classification contexts, surpassing both ViT and ResNet on large-scale benchmarks.

翻译：卫星任务与地球观测（EO）系统是环境监测、灾难事件及时识别以及对自然资源和人造资产（如植被、水体、森林及建筑物）进行长期监测的基础设施。不同EO任务（如MODIS、Sentinel-1和Sentinel-2）能够收集多个光谱波段的信息。因此，借助机器学习、计算机视觉领域的最新进展以及标注数据的可用性，研究人员证明了通过深度神经网络实现土地利用监测系统与遥感图像分类的可行性与精度。此类系统可协助领域专家和政府进行持续的环境监测，从而在发生灾难事件（如偏远地区的森林野火）时实现及时干预。尽管计算机视觉领域近期取得了进展，但许多研究仍局限于分析卷积神经网络（CNN）以及近期出现的视觉Transformer（ViT）。鉴于图神经网络（GNN）在时序数据、图像等非图数据上取得的成功，我们探究了最新视觉GNN架构（ViG）在土地覆盖分类任务中的性能。实验结果表明，ViG在多分类和多标签分类场景中均达到了最先进的性能，在大型基准测试中超越了ViT和ResNet。