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arXiv · 2606.14886

Improved Knowledge Distillation for Land-Use Image Classification

Arundhuti Sur·Abhiroop Chatterjee·Susmita Ghosh·Emmett Ientilucci
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Abstract

In the present article, an improved Knowledge Distillation (KD) framework has been proposed for efficient compression of deep convolutional neural networks for land-use image classification task. Motivated by the need to achieve competitive classification accuracy while reducing computational complexity, a teacher-student learning paradigm is adopted in which a VGG16 network transfers knowledge to a lightweight MobileNetV2 model. The proposed framework integrates hard supervision from ground truth labels with a soft supervision strategy that combines Kullback-Leibler divergence and Cosine Similarity losses. Experiments conducted on three land-use datasets show that the proposed KD-based method yields improved performance, and achieves an accuracy of 99.04%, outperforming both baseline student training and single-loss distillation approaches, while retaining substantial model compression.

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Arundhuti Sur, Abhiroop Chatterjee, Susmita Ghosh, Emmett Ientilucci. 2026-06-12. Improved Knowledge Distillation for Land-Use Image Classification. https://arxiv.org/abs/2606.14886

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