Region-Based Retrieval of Remote Sensing Images Using an Unsupervised Graph-Theoretic Approach

This letter introduces a novel unsupervised graph-theoretic approach in the framework of region-based retrieval of remote sensing (RS) images. The proposed approach is characterized by two main steps: 1) modeling each image by a graph, which provides region-based image representation combining both local information and related spatial organization, and 2) retrieving the images in the archive that are most similar to the query image by evaluating graph-based similarities. In the first step, each image is initially segmented into distinct regions and then modeled by an attributed relational graph, where nodes and edges represent region characteristics and their spatial relationships, respectively. In the second step, a novel inexact graph matching strategy, which jointly exploits a subgraph isomorphism algorithm and a spectral graph embedding technique, is applied to match corresponding graphs and to retrieve images in the order of graph similarity. Experiments carried out on an archive of aerial images point out that the proposed approach significantly improves the retrieval performance compared to the state-of-the-art unsupervised RS image retrieval methods.