training small networks for scene classification of remote sensing images via knowledge distillation

Clicks: 238
ID: 169825
2018
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Abstract
Scene classification, aiming to identify the land-cover categories of remotely sensed image patches, is now a fundamental task in the remote sensing image analysis field. Deep-learning-model-based algorithms are widely applied in scene classification and achieve remarkable performance, but these high-level methods are computationally expensive and time-consuming. Consequently in this paper, we introduce a knowledge distillation framework, currently a mainstream model compression method, into remote sensing scene classification to improve the performance of smaller and shallower network models. Our knowledge distillation training method makes the high-temperature softmax output of a small and shallow student model match the large and deep teacher model. In our experiments, we evaluate knowledge distillation training method for remote sensing scene classification on four public datasets: AID dataset, UCMerced dataset, NWPU-RESISC dataset, and EuroSAT dataset. Results show that our proposed training method was effective and increased overall accuracy (3% in AID experiments, 5% in UCMerced experiments, 1% in NWPU-RESISC and EuroSAT experiments) for small and shallow models. We further explored the performance of the student model on small and unbalanced datasets. Our findings indicate that knowledge distillation can improve the performance of small network models on datasets with lower spatial resolution images, numerous categories, as well as fewer training samples.
Reference Key
chen2018remotetraining Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Guanzhou Chen;Xiaodong Zhang;Xiaoliang Tan;Yufeng Cheng;Fan Dai;Kun Zhu;Yuanfu Gong;Qing Wang
Journal Journal of pharmacological sciences
Year 2018
DOI
10.3390/rs10050719
URL
http://www.mdpi.com/2072-4292/10/5/719
Keywords
remote sensing convolutional neural networks (cnns)

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