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

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.

Automated summary

Scene classification is an important task in remote sensing, and multispectral (MS) images are crucial for better classification. However, they are not always available due to cost and complexity. To address this, a new MS-to-RGB knowledge distillation (MS2RGB-KD) framework is proposed in this letter. It transfers MS knowledge from a teacher model to a student model, improving scene classification performance using only RGB images.