Multispectral-to-RGB Knowledge Distillation for Remote Sensing Image Scene Classification

Hong Kyu Shin, Kwang Hyun Uhm, Seung Won Jung, Sung Jea Ko

Research output: Contribution to journalArticlepeer-review


Scene classification is a fundamental task in the remote sensing (RS) field, assigning semantic labels to RS images. Multispectral (MS) images play an essential role in scene classification as they contain richer spectral information than red, green, blue (RGB) images. However, MS images are not always available due to the higher cost and complexity of MS sensors compared to RGB sensors. To improve scene classification performance using only RGB images, in this letter, we propose a novel MS-to-RGB knowledge distillation (MS2RGB-KD) framework that transfers MS knowledge from a teacher model to a student model. Specifically, our MS2RGB-KD drives a student model that requires only an RGB image as input to mimic the feature representations of different modalities extracted by the teacher model. Moreover, we introduce novel loss functions that encourage the student model to preserve intramodal and intermodal relationships of the feature representations in the teacher model. Experiments on the EuroSAT dataset demonstrate the effectiveness of MS2RGB-KD compared with other KD baselines.

Original languageEnglish
Article number5000805
JournalIEEE Geoscience and Remote Sensing Letters
Publication statusPublished - 2023

Bibliographical note

Publisher Copyright:
© 2004-2012 IEEE.


  • Knowledge distillation (KD)
  • multispectral (MS) image
  • scene classification

ASJC Scopus subject areas

  • Geotechnical Engineering and Engineering Geology
  • Electrical and Electronic Engineering


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