Ensemble Learning techniques for object detection in high-resolution satellite images
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Abstract (arXiv)
Ensembling is a method that aims to maximize the detection performance by fusing individual detectors. While rarely mentioned in deep-learning articles applied to remote sensing, ensembling methods have been widely used to achieve high scores in recent data science com-petitions, such as Kaggle. The few remote sensing articles mentioning ensembling mainly focus on mid resolution images and earth observation applications such as land use classification, but never on Very High Resolution (VHR) images for defense-related applications or object detection.This study aims at reviewing the most relevant ensembling techniques to be used for object detection on very high resolution imagery and shows an example of the value of such techniques on a relevant operational use-case (vehicle detection in desert areas).
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 8 of its 14 references that are in the map. First posted on arXiv 16 Feb 2022.
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References · 8
- 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,416
- 2018Averaging Weights Leads to Wider Optima and Better Generalizationcited by 1,194
- 2018Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNscited by 585
- 2017The Effectiveness of Data Augmentation in Image Classification using Deep Learningcited by 368
- 2017Snapshot Ensembles: Train 1, get M for freecited by 349
- 2017Improving Deep Learning using Generic Data Augmentationcited by 36
- 2018Hydra: an Ensemble of Convolutional Neural Networks for Geospatial Land Classificationcited by 12
- 2016SNN: Stacked Neural Networkscited by 1
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