Normalized Difference Red-Edge Estimation With Modified DiscoGAN Model

Abstract

Vegetation information is important to study the health and productivity of farmlands and forest ecosystems and investigate the types and severity of threats to them. To obtain vegetation information, Normalized Difference Vegetation Index (NDVI) or Normalized Difference Red-Edge (NDRE) is usually used as a single number quantifying vegetation biomass and plant vigor from satellite remote sensing data. Because they indicate different stages of plant growth and focus on different aspects of plant health, the optimal solution for enhancing vegetation information is to use both of them. However, through satellite remote sensing data containing Red, Green, Blue (RGB) and Near-Infrared (NIR) images, we can only calculate the NDVI, not the NDRE that requires the Red-Edge (RE) images. Therefore, in this paper, we propose an NDRE estimation method using the RE images generated from the RGB images by a modified Discover Cross-Domain Relations with Generative Adversarial Networks (DiscoGAN) model. The modified DiscoGAN model was designed by adding some input and hidden layers in generators and discriminators of the original DiscoGAN model to ingest the RGB images with $256 \times 256 \times 3$ dimension and improve the average Normalized Mean Square Error (NMSE) performance. Experimental results showed that the modified DiscoGAN model outperformed the original DiscoGAN model, obtaining the average NMSE of 0.018 between the real RE images and the generated RE images. Moreover, the NDRE estimation method achieved the average NMSE of 0.074 between the real NDRE values and the NDRE estimates.

Keywords

• DiscoGAN
• NDRE
• NDVI
• red-edge
• remote sensing
• satellite image