A Novel Graph-Theoretic Deep Representation Learning Method for Multi-Label Remote Sensing Image Retrieval

TL;DR

This paper introduces a new graph-theoretic deep learning approach for multi-label remote sensing image retrieval, leveraging graph structures to encode label co-occurrences and spatial information for improved retrieval accuracy.

Contribution

It proposes a novel deep learning framework that automatically predicts graph structures for remote sensing images, enhancing multi-label retrieval performance.

Findings

Outperforms state-of-the-art deep representation methods in RS retrieval tasks.

Effectively captures multi-label co-occurrence relationships and spatial information.

Code is publicly available for reproducibility and further research.

Abstract

This paper presents a novel graph-theoretic deep representation learning method in the framework of multi-label remote sensing (RS) image retrieval problems. The proposed method aims to extract and exploit multi-label co-occurrence relationships associated to each RS image in the archive. To this end, each training image is initially represented with a graph structure that provides region-based image representation combining both local information and the related spatial organization. Unlike the other graph-based methods, the proposed method contains a novel learning strategy to train a deep neural network for automatically predicting a graph structure of each RS image in the archive. This strategy employs a region representation learning loss function to characterize the image content based on its multi-label co-occurrence relationship. Experimental results show the effectiveness of the proposed method for retrieval problems in RS compared to state-of-the-art deep representation learning methods. The code of the proposed method is publicly available at https://git.tu-berlin.de/rsim/GT-DRL-CBIR .