# Creating Ensembles of Classifiers through UMDA for Aerial Scene Classification

arXiv: https://arxiv.org/abs/2303.11389 · HTML: https://mlsanity.com/paper/2303.11389/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 70 references that are in the map.
- First posted on arXiv 20 Mar 2023.

## Builds on (most cited first, 19 of 19)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [Remote Sensing Image Scene Classification: Benchmark and State of the Art](https://mlsanity.com/paper/1703.00121/) (2017)
- [AID: A Benchmark Dataset for Performance Evaluation of Aerial Scene Classification](https://mlsanity.com/paper/1608.05167/) (2016)
- [Functional Map of the World](https://mlsanity.com/paper/1711.07846/) (2017)
- [No Fuss Distance Metric Learning using Proxies](https://mlsanity.com/paper/1703.07464/) (2017)
- [Proxy Anchor Loss for Deep Metric Learning](https://mlsanity.com/paper/2003.13911/) (2020)
- [SoftTriple Loss: Deep Metric Learning Without Triplet Sampling](https://mlsanity.com/paper/1909.05235/) (2019)
- [Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities](https://mlsanity.com/paper/2005.01094/) (2020)
- [Land Use Classification in Remote Sensing Images by Convolutional Neural Networks](https://mlsanity.com/paper/1508.00092/) (2015)
- [RS-MetaNet: Deep meta metric learning for few-shot remote sensing scene classification](https://mlsanity.com/paper/2009.13364/) (2020)
- [MARTA GANs: Unsupervised Representation Learning for Remote Sensing Image Classification](https://mlsanity.com/paper/1612.08879/) (2016)
- [Hydra: an Ensemble of Convolutional Neural Networks for Geospatial Land Classification](https://mlsanity.com/paper/1802.03518/) (2018)
- [Enhancing Remote Sensing Image Retrieval with Triplet Deep Metric Learning Network](https://mlsanity.com/paper/1902.05818/) (2019)
- [Mixup-based Deep Metric Learning Approaches for Incomplete Supervision](https://mlsanity.com/paper/2204.13572/) (2022)

## Abstract (arXiv)

Aerial scene classification, which aims to semantically label remote sensing images in a set of predefined classes (e.g., agricultural, beach, and harbor), is a very challenging task in remote sensing due to high intra-class variability and the different scales and orientations of the objects present in the dataset images. In remote sensing area, the use of CNN architectures as an alternative solution is also a reality for scene classification tasks. Generally, these CNNs are used to perform the traditional image classification task. However, another less used way to classify remote sensing image might be the one that uses deep metric learning (DML) approaches. In this sense, this work proposes to employ six DML approaches for aerial scene classification tasks, analysing their behave with four different pre-trained CNNs as well as combining them through the use of evolutionary computation algorithm (UMDA). In performed experiments, it is possible to observe than DML approaches can achieve the best classification results when compared to traditional pre-trained CNNs for three well-known remote sensing aerial scene datasets. In addition, the UMDA algorithm proved to be a promising strategy to combine DML approaches when there is diversity among them, managing to improve at least 5.6% of accuracy in the classification results using almost 50% of the available classifiers for the construction of the final ensemble of classifiers.