A Light-weight Deep Learning Model for Remote Sensing Image Classification

Lam Pham, Cam Le, Dat Ngo, Anh Nguyen, Jasmin Lampert +2 more
2/25/2023
cs.CVcs.AIcs.LG

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Abstract

In this paper, we present a high-performance and light-weight deep learning model for Remote Sensing Image Classification (RSIC), the task of identifying the aerial scene of a remote sensing image. To this end, we first valuate various benchmark convolutional neural network (CNN) architectures: MobileNet V1/V2, ResNet 50/151V2, InceptionV3/InceptionResNetV2, EfficientNet B0/B7, DenseNet 121/201, ConNeXt Tiny/Large. Then, the best performing models are selected to train a compact model in a teacher-student arrangement. The knowledge distillation from the teacher aims to achieve high performance with significantly reduced complexity. By conducting extensive experiments on the NWPU-RESISC45 benchmark, our proposed teacher-student models outperforms the state-of-the-art systems, and has potential to be applied on a wide rage of edge devices.

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Cite this paper

@article{pham2023lightweight,
  title  = {A Light-weight Deep Learning Model for Remote Sensing Image Classification},
  author = {Lam Pham and Cam Le and Dat Ngo and Anh Nguyen and Jasmin Lampert and Alexander Schindler and Ian McLoughlin},
  year   = {2023},
  eprint = {2302.13028},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2302.13028v1}
}

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