# Informative and Representative Triplet Selection for Multilabel Remote Sensing Image Retrieval

arXiv: https://arxiv.org/abs/2105.03647 · HTML: https://mlsanity.com/paper/2105.03647/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 41 references that are in the map.
- First posted on arXiv 8 May 2021.

## Builds on (most cited first, 20 of 20)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Deep Metric Learning via Lifted Structured Feature Embedding](https://mlsanity.com/paper/1511.06452/) (2015)
- [Deep metric learning using Triplet network](https://mlsanity.com/paper/1412.6622/) (2014)
- [Multi-Similarity Loss with General Pair Weighting for Deep Metric Learning](https://mlsanity.com/paper/1904.06627/) (2019)
- [Sampling Matters in Deep Embedding Learning](https://mlsanity.com/paper/1706.07567/) (2017)
- [BigEarthNet: A Large-Scale Benchmark Archive For Remote Sensing Image Understanding](https://mlsanity.com/paper/1902.06148/) (2019)
- [Deep Metric Learning with Hierarchical Triplet Loss](https://mlsanity.com/paper/1810.06951/) (2018)
- [Ranked List Loss for Deep Metric Learning](https://mlsanity.com/paper/1903.03238/) (2019)
- [Improved Embeddings with Easy Positive Triplet Mining](https://mlsanity.com/paper/1904.04370/) (2019)
- [Simultaneous Feature Learning and Hash Coding with Deep Neural Networks](https://mlsanity.com/paper/1504.03410/) (2015)
- [Hard-Aware Deeply Cascaded Embedding](https://mlsanity.com/paper/1611.05720/) (2016)
- [Deep Metric Learning Beyond Binary Supervision](https://mlsanity.com/paper/1904.09626/) (2019)
- [Deep Metric Learning with Spherical Embedding](https://mlsanity.com/paper/2011.02785/) (2020)
- [Deep Randomized Ensembles for Metric Learning](https://mlsanity.com/paper/1808.04469/) (2018)
- [Metric-Learning based Deep Hashing Network for Content Based Retrieval of Remote Sensing Images](https://mlsanity.com/paper/1904.01258/) (2019)
- [Enhancing Remote Sensing Image Retrieval with Triplet Deep Metric Learning Network](https://mlsanity.com/paper/1902.05818/) (2019)
- [Deep Learning for Image Search and Retrieval in Large Remote Sensing Archives](https://mlsanity.com/paper/2004.01613/) (2020)
- [Aggregated Deep Local Features for Remote Sensing Image Retrieval](https://mlsanity.com/paper/1903.09469/) (2019)

## Built on by (most cited first, 9 of 9)

- [Benchmarking and scaling of deep learning models for land cover image classification](https://mlsanity.com/paper/2111.09451/) (2021)
- [Composed Image Retrieval for Remote Sensing](https://mlsanity.com/paper/2405.15587/) (2024)
- [Deep Unsupervised Contrastive Hashing for Large-Scale Cross-Modal Text-Image Retrieval in Remote Sensing](https://mlsanity.com/paper/2201.08125/) (2022)
- [Generative Reasoning Integrated Label Noise Robust Deep Image Representation Learning](https://mlsanity.com/paper/2212.01261/) (2022)
- [Triplet Loss-less Center Loss Sampling Strategies in Facial Expression Recognition Scenarios](https://mlsanity.com/paper/2302.04108/) (2023)
- [Multimorbidity Content-Based Medical Image Retrieval Using Proxies](https://mlsanity.com/paper/2211.12185/) (2022)
- [Annotation Cost-Efficient Active Learning for Deep Metric Learning Driven Remote Sensing Image Retrieval](https://mlsanity.com/paper/2406.10107/) (2024)
- [MARC: Multi-Label Adaptive Retrieval Contrastive Loss for Remote Sensing Images](https://mlsanity.com/paper/2512.16294/) (2025)
- [Benchmarking Composed Image Retrieval for Applied Earth Observation](https://mlsanity.com/paper/2605.24442/) (2026)

## Abstract (arXiv)

Learning the similarity between remote sensing (RS) images forms the foundation for content-based RS image retrieval (CBIR). Recently, deep metric learning approaches that map the semantic similarity of images into an embedding (metric) space have been found very popular in RS. A common approach for learning the metric space relies on the selection of triplets of similar (positive) and dissimilar (negative) images to a reference image called as an anchor. Choosing triplets is a difficult task particularly for multi-label RS CBIR, where each training image is annotated by multiple class labels. To address this problem, in this paper we propose a novel triplet sampling method in the framework of deep neural networks (DNNs) defined for multi-label RS CBIR problems. The proposed method selects a small set of the most representative and informative triplets based on two main steps. In the first step, a set of anchors that are diverse to each other in the embedding space is selected from the current mini-batch using an iterative algorithm. In the second step, different sets of positive and negative images are chosen for each anchor by evaluating the relevancy, hardness and diversity of the images among each other based on a novel strategy. Experimental results obtained on two multi-label benchmark archives show that the selection of the most informative and representative triplets in the context of DNNs results in: i) reducing the computational complexity of the training phase of the DNNs without any significant loss on the performance; and ii) an increase in learning speed since informative triplets allow fast convergence. The code of the proposed method is publicly available at https://git.tu-berlin.de/rsim/image-retrieval-from-triplets.