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October 3, 2025Open Access

Improving Remote Sensing Classification using Topological Data Analysis and Convolutional Neural Networks

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The integration of topological data analysis features improves model accuracy by 1.44% on the EuroSAT dataset, achieving 99.33%.
Using a ResNet18 architecture, the classification accuracy surpasses previous single-model records, including larger models.
This is the first time topological data analysis features have been used in deep learning for satellite scene classification.
Our approach demonstrates the feasibility of applying topological features in datasets lacking explicit geometric structures.

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Implication

Our method improves classification accuracy in remote sensing using topological data analysis and CNNs, suggesting new possibilities in feature integration.

Cite This Study

Aaryam Sharma (2025) studied this question.

synapsesocial.com/papers/68e040e5a99c246f578b2ea3 https://doi.org/10.48550/arxiv.2507.10381

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Implication

Our method improves classification accuracy in remote sensing using topological data analysis and CNNs, suggesting new possibilities in feature integration.

Improving Remote Sensing Classification using Topological Data Analysis and Convolutional Neural Networks

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Discussion

Implication

Cite This Study

Improving Remote Sensing Classification using Topological Data Analysis and Convolutional Neural Networks

Key Points

Discussion

Implication

Cite This Study

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