Adding simulated NDVI images can be effective for identifying crop types from Sentinel-1 C-SAR data

Abstract

The Normalized Difference Vegetation Index (NDVI) has been used for evaluating various vegetation properties and then it is also effective for improving classification accuracies. However, optical remote sensing imagery is limited by cloud contamination. In this study, NDVI images were simulated using the image-to-image translation methods including CycleGAN, pix2pix and pix2pixHD and then they were evaluated for classifying crop types. A significant improvement was confirmed by adding NDVI images generated by pix2pix or pix2pixHD on Sentinel-1 C-SAR VH/VV polarization data and resulted in overall accuracies of 68.0%.