Rs-sskd: Self-supervision equipped with knowledge distillation for few-shot remote sensing scene classification

Pei Zhang, Ying Li, Dong Wang, Jiyue Wang

Research output: Contribution to journalArticlepeer-review

20 Scopus citations

Abstract

While growing instruments generate more and more airborne or satellite images, the bottleneck in remote sensing (RS) scene classification has shifted from data limits toward a lack of ground truth samples. There are still many challenges when we are facing unknown environments, especially those with insufficient training data. Few-shot classification offers a different picture under the umbrella of meta-learning: digging rich knowledge from a few data are possible. In this work, we propose a method named RS-SSKD for few-shot RS scene classification from a perspective of generating powerful representation for the downstream meta-learner. Firstly, we propose a novel two-branch network that takes three pairs of original-transformed images as inputs and incorporates Class Activation Maps (CAMs) to drive the network mining, the most relevant category-specific region. This strategy ensures that the network generates discriminative embeddings. Secondly, we set a round of self-knowledge distillation to prevent overfitting and boost the performance. Our experiments show that the proposed method surpasses current state-of-the-art approaches on two challenging RS scene datasets: NWPU-RESISC45 and RSD46-WHU. Finally, we conduct various ablation experiments to investigate the effect of each component of the proposed method and analyze the training time of state-of-the-art methods and ours.

Original languageEnglish
Article number1566
Pages (from-to)1-23
Number of pages23
JournalSensors
Volume21
Issue number5
DOIs
StatePublished - 1 Mar 2021

Keywords

  • Few-shot learning
  • Knowledge distillation
  • Meta-learning
  • Remote-sensing
  • Scene classification
  • Self-supervised

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