Efficient remote sensing image retrieval based on building contours and graph similarity

With the rapid growth of high-resolution remote sensing data, efficient and accurate image retrieval has become increasingly important for urban analysis and land management. Traditional retrieval approaches based on global or local features often suffer from low efficiency and limited robustness when applied to large-scale, multi-source datasets. To address these challenges, we propose a multi-scale image retrieval framework that integrates optimized building contour analysis with graph-based similarity measurement. Building footprints are first extracted using hybrid CNN – Transformer segmentation and then refined through a series of contour optimization steps, including adaptive simplification, rectification, and structural reconstruction. The optimized polygons are encoded by shape descriptors for preliminary screening, while a Siamese network further evaluates polygon similarity. At the final stage, building distribution is modelled as a labelled undirected graph, and an improved graph edit distance algorithm is applied for accurate matching. The framework was evaluated on Google, Bing, and ArcGIS satellite imagery as well as several public datasets. Experimental results demonstrate that the proposed method substantially improves retrieval accuracy and computational efficiency, while preserving geometric fidelity of complex urban structures. This study highlights the potential of contour- and graph-based approaches for advancing remote sensing image retrieval, with promising applications in urban planning, environmental monitoring, and disaster assessment.