Satellite Image Search in AgoraEO

TL;DR

This paper presents MiLaN, a deep hashing network for fast, accurate satellite image retrieval, integrated into EarthQube for interactive exploration of large EO archives.

Contribution

It introduces MiLaN, a novel deep hashing approach for real-time satellite image similarity search, integrated into a user-friendly search engine within AgoraEO.

Findings

MiLaN enables real-time nearest neighbor search in large satellite image archives.

The integrated EarthQube system supports interactive visual exploration and Query-by-Example.

MiLaN achieves high accuracy in semantic satellite image retrieval.

Abstract

The growing operational capability of global Earth Observation (EO) creates new opportunities for data-driven approaches to understand and protect our planet. However, the current use of EO archives is very restricted due to the huge archive sizes and the limited exploration capabilities provided by EO platforms. To address this limitation, we have recently proposed MiLaN, a content-based image retrieval approach for fast similarity search in satellite image archives. MiLaN is a deep hashing network based on metric learning that encodes high-dimensional image features into compact binary hash codes. We use these codes as keys in a hash table to enable real-time nearest neighbor search and highly accurate retrieval. In this demonstration, we showcase the efficiency of MiLaN by integrating it with EarthQube, a browser and search engine within AgoraEO. EarthQube supports interactive visual exploration and Query-by-Example over satellite image repositories. Demo visitors will interact with EarthQube playing the role of different users that search images in a large-scale remote sensing archive by their semantic content and apply other filters.