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State-of-the-Art Deep Learning Methods for Objects Detection in Remote Sensing Satellite Images.

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Hybridizing Deep Neural Networks and Machine Learning Models for Aerial Satellite Forest Image Segmentation.

Clopas Kwenda1, Mandlenkosi Gwetu2, Jean Vincent Fonou-Dombeu1

  • 1School of Mathematics, Statistics and Computer Science, University of KwaZulu Natal, Pietermaritzburg 3209, South Africa.

Journal of Imaging
|June 26, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a hybrid deep learning and machine learning model for accurate forest cover segmentation from aerial images. The novel approach significantly improves segmentation performance, outperforming existing methods.

Keywords:
deep learningmachine learningsegmentationsupervised approach

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Area of Science:

  • Remote Sensing and Geospatial Analysis
  • Machine Learning and Artificial Intelligence
  • Environmental Monitoring and Climate Change Mitigation

Background:

  • Accurate forest cover monitoring is crucial for climate change mitigation and socio-economic activities.
  • Traditional image segmentation methods struggle with spatial and textural feature extraction, leading to suboptimal forest cover classification.
  • Deep neural networks offer advanced feature extraction capabilities but often require substantial data and computational resources.

Purpose of the Study:

  • To develop and evaluate a novel hybrid approach combining deep neural networks (VGG16, ResNet50) and machine learning classifiers (Random Forest, LSVM, kNN, LDA, GNB) for aerial satellite image segmentation.
  • To enhance the accuracy and efficiency of forest and non-forest region segmentation.
  • To compare the performance of the hybrid model against traditional methods and existing studies.

Main Methods:

  • Feature extraction from aerial satellite forest images using pre-trained VGG16 and ResNet50 deep neural network models.
  • Segmentation of forest and non-forest regions using five machine learning classifiers (Random Forest, LSVM, kNN, LDA, GNB) trained on extracted deep features.
  • Performance evaluation using Accuracy, Jaccard index, and Root Mean Square Error (RMSE) on a deep globe challenge dataset.

Main Results:

  • The hybrid Random Forest model achieved the highest performance with 94% accuracy, 0.913 Jaccard index, and 0.245 RMSE.
  • The hybrid approach significantly improved the segmentation performance of all tested machine learning classifiers compared to their standalone use.
  • The proposed model demonstrated superior segmentation capabilities, outperforming other models in related studies.

Conclusions:

  • The hybrid deep learning and machine learning approach offers a powerful and effective solution for accurate forest cover segmentation from aerial imagery.
  • The integration of deep neural networks for feature extraction enhances the performance of traditional machine learning classifiers for this task.
  • This methodology provides a robust tool for environmental monitoring and climate change research.