Learning-Based Near-Infrared Band Simulation with Applications on Large-Scale Landcover Classification

• Main Authors: Reinartz, P. (Author), Tian, J. (Author), Yuan, X. (Author)
• Format: Article
• Language: English
• Published: MDPI 2023
• Subjects: cGAN; Conditional generative adversarial network; Generative adversarial networks; Infrared devices; multispectral; Multi-spectral; Near Infrared; Near-infrared; NIR; remote sensing; Remote sensing; Remote-sensing; RGB; robust loss; Robust loss; SEN12MS; Sentinel-2; SSIM; Vegetation
• Online Access: View Fulltext in Publisher; View in Scopus

 Multispectral sensors are important instruments for Earth observation. In remote sensing applications, the near-infrared (NIR) band, together with the visible spectrum (RGB), provide abundant information about ground objects. However, the NIR band is typically not available on low-cost camera systems, which presents challenges for the vegetation extraction. To this end, this paper presents a conditional generative adversarial network (cGAN) method to simulate the NIR band from RGB bands of Sentinel-2 multispectral data. We adapt a robust loss function and a structural similarity index loss (SSIM) in addition to the GAN loss to improve the model performance. With 45,529 multi-seasonal test images across the globe, the simulated NIR band had a mean absolute error of 0.02378 and an SSIM of 89.98%. A rule-based landcover classification using the simulated normalized difference vegetation index (NDVI) achieved a Jaccard score of 89.50%. The evaluation metrics demonstrated the versatility of the learning-based paradigm in remote sensing applications. Our simulation approach is flexible and can be easily adapted to other spectral bands. © 2023 by the authors.