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  Published Paper Details:

  Paper Title

Seeing Beyond Pixels: A Hybrid Teacher-Student CNN Approach for Satellite Image Classification

  Authors

  Mayuresh Bhakare,  Tanaya Naik,  Sanika Kalyankar,  Tejal Gurav,  Shashank Tolye

  Keywords

Satellite Image Classification, Knowledge Distillation, Teacher-Student Learning, Convolutional Neural Networks (CNN), ResNet50, VGG16, EfficientNet-B0, Hybrid Loss Function, Remote Sensing, Computational Efficiency

  Abstract


The classification of satellite images is vital in remote sensing for tasks such as mapping land cover, monitoring disasters, and conducting environmental assessments. Nonetheless, deep learning models frequently encounter substantial computational requirements, which hinder their use in real-time settings. To mitigate this issue, we introduce an Embedded Teacher-Student CNN framework that utilizes knowledge distillation for more efficient classification of satellite images. ResNet50 and VGG16 act as teacher models, capturing spatial features and producing soft labels to train a lightweight EfficientNet-B0 student model. Experimental results demonstrate high classification accuracy with reduced inference time, making the model suitable for real-time and resource-constrained applications.

  IJCRT's Publication Details

  Unique Identification Number - IJCRT2504860

  Paper ID - 280655

  Page Number(s) - h277-h283

  Pubished in - Volume 13 | Issue 4 | April 2025

  DOI (Digital Object Identifier) -   

  Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882

  E-ISSN Number - 2320-2882

  Cite this article

  Mayuresh Bhakare,  Tanaya Naik,  Sanika Kalyankar,  Tejal Gurav,  Shashank Tolye,   "Seeing Beyond Pixels: A Hybrid Teacher-Student CNN Approach for Satellite Image Classification", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.13, Issue 4, pp.h277-h283, April 2025, Available at :http://www.ijcrt.org/papers/IJCRT2504860.pdf

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ISSN: 2320-2882
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Journal Starting Year (ESTD) : 2013
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ISSN and 7.97 Impact Factor Details


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ISSN
ISSN: 2320-2882
Impact Factor: 7.97 and ISSN APPROVED
Journal Starting Year (ESTD) : 2013
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