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Related Experiment Video

Updated: Jul 26, 2025

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
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Roof type classification with innovative machine learning approaches.

Naim Ölçer1, Didem Ölçer1, Emre Sümer1

  • 1Department of Computer Engineering, Başkent University, Ankara, Turkey.

Peerj. Computer Science
|June 22, 2023
PubMed
Summary

One-shot learning with Siamese neural networks can classify roof types from satellite images using minimal data. This approach achieved 66% accuracy on real roof images, overcoming the need for large datasets in convolutional neural network methods.

Keywords:
ClassificationDeep learningOne shot learningRoof type

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Area of Science:

  • Computer Science
  • Remote Sensing
  • Artificial Intelligence

Background:

  • Convolutional neural networks (CNNs) are widely used for image classification but require extensive training data.
  • The scarcity of labeled roof images poses a significant challenge for CNN-based roof type classification.
  • One-shot learning mimics human learning by enabling classification with very few examples per category.

Purpose of the Study:

  • To investigate the effectiveness of one-shot learning for roof type classification using Siamese neural networks.
  • To compare the performance of a Siamese neural network model against a traditional CNN model for this task.
  • To address the data scarcity issue in roof type classification.

Main Methods:

  • Utilized a Siamese neural network architecture for one-shot learning.
  • Generated artificial images for training due to the difficulty in obtaining real roof data.
  • Employed a dataset of real roof images (flat, gable, hip) for testing.
  • Trained both a CNN-based model and a Siamese neural network model on the same dataset.

Main Results:

  • The Siamese neural network model, trained on artificially generated images, achieved an average classification success rate of 66% on real roof images.
  • This demonstrates the potential of one-shot learning in scenarios with limited training data.
  • Performance comparison between the Siamese network and the CNN model was conducted.

Conclusions:

  • One-shot learning, particularly with Siamese neural networks, offers a viable solution for roof type classification when training data is scarce.
  • The 66% accuracy achieved highlights the practical applicability of this method in remote sensing and urban planning.
  • Further research can explore more sophisticated data augmentation and network architectures to improve performance.