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Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging

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Deep Ensembling of Multiband Images for Earth Remote Sensing and Foramnifera Data.

Loris Nanni¹, Sheryl Brahnam², Matteo Ruta¹

¹Department of Information Engineering, University of Padova, 35139 Padova, Italy.

Sensors (Basel, Switzerland)

|April 12, 2025

Summary

This summary is machine-generated.

This study introduces an ensemble of neural networks for classifying multiband satellite images. The method significantly improves accuracy in remote sensing and species identification tasks.

Keywords:

convolutional neural network ensemble learning image classification multichannel image satellite images

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Area of Science:

Remote Sensing
Environmental Monitoring
Machine Learning

Background:

Advanced sensors capture high-dimensional multiband images crucial for Earth observation.
Classifying these complex datasets presents significant challenges for accuracy and efficiency.

Purpose of the Study:

To develop and evaluate an ensemble of neural networks for enhanced multiband image classification.
To demonstrate the system's efficacy on satellite imagery and species identification.

Main Methods:

Utilized an ensemble of Convolutional Neural Networks (CNNs) including ResNet50, MobileNetV2, and DenseNet201.
Developed custom ResNet50-based and attention-based networks for direct multiband image input.
Implemented the system using MATLAB 2024b and PyTorch 2.6.

Main Results:

Achieved superior classification accuracy compared to existing advanced methods.
Outperformed human experts in species-level identification of planktic foraminifera (>92% vs. 83%).
Demonstrated state-of-the-art performance on the EuroSAT and LCZ42 datasets.

Conclusions:

Ensemble learning with diverse neural network architectures effectively handles complex multiband image data.
The proposed system offers a robust solution for remote sensing and biological classification tasks.