Affiliation:
1. German Aerospace Center (DLR), Münchner Str. 20, 82234 Weßling, Germany
Abstract
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.
Funder
German Academic Exchange Service
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
Reference54 articles.
1. The Harmonized Landsat and Sentinel-2 surface reflectance dataset;Claverie;Remote Sens. Environ.,2018
2. Clerc and MPC Team (2023, April 09). S2 MPC L1C Data Quality Report. Available online: https://sentinel.esa.int/documents/247904/685211/Sentinel-2_L1C_Data_Quality_Report.
3. Getting simultaneous red and near-infrared band data from a single digital camera for plant monitoring applications: Theoretical and practical study;Rabatel;Biosyst. Eng.,2014
4. Commercial off-the-shelf digital cameras on unmanned aerial vehicles for multitemporal monitoring of vegetation reflectance and NDVI;Berra;IEEE Trans. Geosci. Remote Sens.,2017
5. Small format digital sensors for aerial imaging applications;Dare;Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.,2008