NAS-Based CNN Channel Pruning for Remote Sensing Scene Classification

Xin Wei, Ning Zhang, Wenchao Liu, He Chen*

*Corresponding author for this work

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Abstract

Recently, convolutional neural network (CNN)-based remote sensing scene classification has achieved great success. However, the prohibitively expensive computation and storage requirements of state-of-the-art models have hindered the deployment of CNNs on on- board platforms. In this letter, we propose a differentiable neural architecture search (NAS)-based channel pruning method to automatically prune the CNN models. In the proposed method, the importance of each output channel is measured by a trainable score. The scores are optimized by an NAS method to search a good-performance pruned structure. After the search process, a global score threshold is adopted to derive the pruned model. A cost-awareness loss is proposed for the search process to encourage the floating-point operation (FLOP) compression ratio of the pruned model coverage to a desired value. We apply the proposed method to ResNet-34 and VGG-16 to verify the performance. The NWPU-RESISC-45 and UC Merced Land-Use (UCM) datasets are used for the performance evaluation. A comparison with state-of-the-art pruning methods demonstrates that the proposed method can achieve competitive performance with a similar reduction in FLOP.

Original languageEnglish
Article number6508605
JournalIEEE Geoscience and Remote Sensing Letters
Volume19
DOIs
Publication statusPublished - 2022

Keywords

  • Channel pruning
  • convolutional neural network (CNN)
  • neural architecture search (NAS)
  • remote sensing scene classification

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Wei, X., Zhang, N., Liu, W., & Chen, H. (2022). NAS-Based CNN Channel Pruning for Remote Sensing Scene Classification. IEEE Geoscience and Remote Sensing Letters, 19, Article 6508605. https://doi.org/10.1109/LGRS.2022.3165841