# Remote Sensing Image Classification Using Deep Ensemble Learning

arXiv: https://arxiv.org/abs/2603.05844 · HTML: https://mlsanity.com/paper/2603.05844/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 69 references that are in the map.
- First posted on arXiv 6 Mar 2026.

## Builds on (most cited first, 18 of 18)

- [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)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Training data-efficient image transformers & distillation through attention](https://mlsanity.com/paper/2012.12877/) (2020)
- [Sigmoid Loss for Language Image Pre-Training](https://mlsanity.com/paper/2303.15343/) (2023)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016)
- [CvT: Introducing Convolutions to Vision Transformers](https://mlsanity.com/paper/2103.15808/) (2021)
- [XCiT: Cross-Covariance Image Transformers](https://mlsanity.com/paper/2106.09681/) (2021)
- [Multimodal Fusion Transformer for Remote Sensing Image Classification](https://mlsanity.com/paper/2203.16952/) (2022)
- [Geographical Knowledge-driven Representation Learning for Remote Sensing Images](https://mlsanity.com/paper/2107.05276/) (2021)
- [Ensembles of Vision Transformers as a New Paradigm for Automated Classification in Ecology](https://mlsanity.com/paper/2203.01726/) (2022)
- [SynthEnsemble: A Fusion of CNN, Vision Transformer, and Hybrid Models for Multi-Label Chest X-Ray Classification](https://mlsanity.com/paper/2311.07750/) (2023)

## Built on by (most cited first, 1 of 1)

- [MLLM-Routed Heterogeneous Ensembles for Robust Cross-Dataset Image Classification](https://mlsanity.com/paper/2608.13463/) (2026)

## Abstract (arXiv)

Remote sensing imagery plays a crucial role in many applications and requires accurate computerized classification techniques. Reliable classification is essential for transforming raw imagery into structured and usable information. While Convolutional Neural Networks (CNNs) are mostly used for image classification, they excel at local feature extraction, but struggle to capture global contextual information. Vision Transformers (ViTs) address this limitation through self attention mechanisms that model long-range dependencies. Integrating CNNs and ViTs, therefore, leads to better performance than standalone architectures. However, the use of additional CNN and ViT components does not lead to further performance improvement and instead introduces a bottleneck caused by redundant feature representations. In this research, we propose a fusion model that combines the strengths of CNNs and ViTs for remote sensing image classification. To overcome the performance bottleneck, the proposed approach trains four independent fusion models that integrate CNN and ViT backbones and combine their outputs at the final prediction stage through ensembling. The proposed method achieves accuracy rates of 98.10 percent, 94.46 percent, and 95.45 percent on the UC Merced, RSSCN7, and MSRSI datasets, respectively. These results outperform competing architectures and highlight the effectiveness of the proposed solution, particularly due to its efficient use of computational resources during training.