# Metric-Learning based Deep Hashing Network for Content Based Retrieval of Remote Sensing Images

## TL;DR

This paper introduces a metric-learning based deep hashing network for remote sensing image retrieval, which effectively learns semantic feature spaces and compact binary codes, significantly improving retrieval accuracy over existing methods.

## Contribution

It proposes a novel deep hashing network that jointly learns a semantic metric space and binary codes, enhancing retrieval performance in remote sensing images.

## Key findings

- Significant improvement in retrieval accuracy over state-of-the-art methods.
- Effective joint learning of semantic space and binary codes.
- Maintains fast search speed with improved performance.

## Abstract

Hashing methods have been recently found very effective in retrieval of remote sensing (RS) images due to their computational efficiency and fast search speed. The traditional hashing methods in RS usually exploit hand-crafted features to learn hash functions to obtain binary codes, which can be insufficient to optimally represent the information content of RS images. To overcome this problem, in this paper we introduce a metric-learning based hashing network, which learns: 1) a semantic-based metric space for effective feature representation; and 2) compact binary hash codes for fast archive search. Our network considers an interplay of multiple loss functions that allows to jointly learn a metric based semantic space facilitating similar images to be clustered together in that target space and at the same time producing compact final activations that lose negligible information when binarized. Experiments carried out on two benchmark RS archives point out that the proposed network significantly improves the retrieval performance under the same retrieval time when compared to the state-of-the-art hashing methods in RS.