Hierarchical Multiscale Representation in Remote Sensing Scene Classification

Remote sensing scene classification (RSSC) poses significant challenges due to high spatial variability, complex textures, and semantic ambiguity in remote sensing imagery. While convolutional neural networks (CNNs) and transformer-based models have achieved notable success in this domain, their performance often depends on large-scale pretraining and substantial computational resources. Graph neural networks (GNNs) have emerged as a promising alternative to traditional deep learning methods by explicitly modeling the relational structure of image regions through graph representations, which have already demonstrated promising results across various image-based tasks involving images. In this work, we explore two GNN architectures tailored for RSSC: BRMv2, a novel simplified graph model built on a base region adjacency graph (RAG), and modified hierarchical layered multigraph network (mHELMNet), a modified hierarchical multigraph model that encodes multiscale and spatial relationships through a multigraph representation. Both models were evaluated on the EUROSAT and RESISC45 datasets, achieving accuracy comparable to, or in some cases exceeding, that of state-of-the-art CNN-based, hybrid GNN-based, and transformer-based methods, while using significantly fewer parameters and without relying on pretraining. Experimental results demonstrated that the proposed GNN models, mHELMNet and BRMv2, achieved over 96% accuracy on EUROSAT and approximately 85% on RESISC45, while requiring only 0.14% and 0.03% of the parameters of the leading transformer-based approach, respectively.