# CMIR-NET : A Deep Learning Based Model For Cross-Modal Retrieval In Remote Sensing

arXiv: https://arxiv.org/abs/1904.04794 · HTML: https://mlsanity.com/paper/1904.04794/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Apr 2019.

## Built on by (most cited first, 7 of 7)

- [Remote Sensing Cross-Modal Text-Image Retrieval Based on Global and Local Information](https://mlsanity.com/paper/2204.09860/) (2022)
- [Exploring Masked Autoencoders for Sensor-Agnostic Image Retrieval in Remote Sensing](https://mlsanity.com/paper/2401.07782/) (2024)
- [A Novel Self-Supervised Cross-Modal Image Retrieval Method In Remote Sensing](https://mlsanity.com/paper/2202.11429/) (2022)
- [CrossATNet - A Novel Cross-Attention Based Framework for Sketch-Based Image Retrieval](https://mlsanity.com/paper/2104.09918/) (2021)
- [SMART-Ship: A Comprehensive Synchronized Multi-modal Aligned Remote Sensing Targets Dataset and Benchmark for Berthed Ships Analysis](https://mlsanity.com/paper/2508.02384/) (2025)
- [MOON: Multi-Hash Codes Joint Learning for Cross-Media Retrieval](https://mlsanity.com/paper/2109.00883/) (2021)
- [CR-JEPA: Cross-Modal Joint-Embedding Predictive Learning for Remote Sensing Image Retrieval](https://mlsanity.com/paper/2606.00706/) (2026)

## Abstract (arXiv)

We address the problem of cross-modal information retrieval in the domain of remote sensing. In particular, we are interested in two application scenarios: i) cross-modal retrieval between panchromatic (PAN) and multi-spectral imagery, and ii) multi-label image retrieval between very high resolution (VHR) images and speech based label annotations. Notice that these multi-modal retrieval scenarios are more challenging than the traditional uni-modal retrieval approaches given the inherent differences in distributions between the modalities. However, with the growing availability of multi-source remote sensing data and the scarcity of enough semantic annotations, the task of multi-modal retrieval has recently become extremely important. In this regard, we propose a novel deep neural network based architecture which is considered to learn a discriminative shared feature space for all the input modalities, suitable for semantically coherent information retrieval. Extensive experiments are carried out on the benchmark large-scale PAN - multi-spectral DSRSID dataset and the multi-label UC-Merced dataset. Together with the Merced dataset, we generate a corpus of speech signals corresponding to the labels. Superior performance with respect to the current state-of-the-art is observed in all the cases.