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

arXiv: https://arxiv.org/abs/1904.01258 · HTML: https://mlsanity.com/paper/1904.01258/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Apr 2019.

## Built on by (most cited first, 13 of 13)

- [BigEarthNet-MM: A Large Scale Multi-Modal Multi-Label Benchmark Archive for Remote Sensing Image Classification and Retrieval](https://mlsanity.com/paper/2105.07921/) (2021)
- [SelaVPR++: Towards Seamless Adaptation of Foundation Models for Efficient Place Recognition](https://mlsanity.com/paper/2502.16601/) (2025)
- [Deep Unsupervised Contrastive Hashing for Large-Scale Cross-Modal Text-Image Retrieval in Remote Sensing](https://mlsanity.com/paper/2201.08125/) (2022)
- [Informative and Representative Triplet Selection for Multilabel Remote Sensing Image Retrieval](https://mlsanity.com/paper/2105.03647/) (2021)
- [Few-shot Action Recognition with Captioning Foundation Models](https://mlsanity.com/paper/2310.10125/) (2023)
- [A Novel Graph-Theoretic Deep Representation Learning Method for Multi-Label Remote Sensing Image Retrieval](https://mlsanity.com/paper/2106.00506/) (2021)
- [Multi-Spectral Remote Sensing Image Retrieval Using Geospatial Foundation Models](https://mlsanity.com/paper/2403.02059/) (2024)
- [Unsupervised Contrastive Hashing for Cross-Modal Retrieval in Remote Sensing](https://mlsanity.com/paper/2204.08707/) (2022)
- [Satellite Image Search in AgoraEO](https://mlsanity.com/paper/2208.10830/) (2022)
- [Credible Remote Sensing Scene Classification Using Evidential Fusion on Aerial-Ground Dual-view Images](https://mlsanity.com/paper/2301.00622/) (2023)
- [A Novel Framework to Jointly Compress and Index Remote Sensing Images for Efficient Content-Based Retrieval](https://mlsanity.com/paper/2201.06459/) (2022)
- [A Triplet-loss Dilated Residual Network for High-Resolution Representation Learning in Image Retrieval](https://mlsanity.com/paper/2303.08398/) (2023)
- [Annotation Cost-Efficient Active Learning for Deep Metric Learning Driven Remote Sensing Image Retrieval](https://mlsanity.com/paper/2406.10107/) (2024)

## Abstract (arXiv)

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.