# Aggregated Deep Local Features for Remote Sensing Image Retrieval

arXiv: https://arxiv.org/abs/1903.09469 · HTML: https://mlsanity.com/paper/1903.09469/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Mar 2019.

## Built on by (most cited first, 5 of 5)

- [Composed Image Retrieval for Remote Sensing](https://mlsanity.com/paper/2405.15587/) (2024)
- [Informative and Representative Triplet Selection for Multilabel Remote Sensing Image Retrieval](https://mlsanity.com/paper/2105.03647/) (2021)
- [Unifying Remote Sensing Image Retrieval and Classification with Robust Fine-tuning](https://mlsanity.com/paper/2102.13392/) (2021)
- [Leveraging Convolutional and Graph Networks for an Unsupervised Remote Sensing Labelling Tool](https://mlsanity.com/paper/2508.00506/) (2025)
- [Benchmarking Composed Image Retrieval for Applied Earth Observation](https://mlsanity.com/paper/2605.24442/) (2026)

## Abstract (arXiv)

Remote Sensing Image Retrieval remains a challenging topic due to the special nature of Remote Sensing Imagery. Such images contain various different semantic objects, which clearly complicates the retrieval task. In this paper, we present an image retrieval pipeline that uses attentive, local convolutional features and aggregates them using the Vector of Locally Aggregated Descriptors (VLAD) to produce a global descriptor. We study various system parameters such as the multiplicative and additive attention mechanisms and descriptor dimensionality. We propose a query expansion method that requires no external inputs. Experiments demonstrate that even without training, the local convolutional features and global representation outperform other systems. After system tuning, we can achieve state-of-the-art or competitive results. Furthermore, we observe that our query expansion method increases overall system performance by about 3%, using only the top-three retrieved images. Finally, we show how dimensionality reduction produces compact descriptors with increased retrieval performance and fast retrieval computation times, e.g. 50% faster than the current systems.