Enhancing NDVI Calculation of Low-Resolution Imagery using ESRGANs

Abstract

Normalized Difference Vegetation Index (NDVI) has been one of the key scales for monitoring multiple plant parameters, but satellite imagery is never up to date, which makes it difficult to get readings for the recent situation of field crops. Doing so with Unmanned Aerial System, drone, in this case, is an intricate task, but with its advantages which include timely and effective measurements with the least errors to be fixed in post-processing of data. Before this, NDVI has been calculated using an Unmanned Aerial System, but the problem of the low resolution of the imagery always lingers. With the recent advancement of generated adversarial networks, the up-scaling of images has been made possible, which, if done with the right model, rules out the need for upgrading the camera hardware that is never cost-effective. We have come up with the solution of calculating the vegetation index of field crops by implementing Enhanced Super-Resolution Generated Adversarial Networks with drone imagery to calculate the vegetation index of crop fields. A simple near-infrared spectrum camera is usually not capable of producing a higher resolution image, by implementing the aforementioned generated adversarial network, we have been able to calculate vegetation index for a comparably much higher resolution image without upgrading with sophisticated hardware. We were able to perform the calculations for more pixels (12952) against the same area yielded an output value of 0.829 as compared to 0.828 in the case of low-resolution imagery (546416 pixels). The averaged values for red and near-infrared pixels showed changes from 32.337 to 30.264 for red, and from 189.168 to 182.1656 for near-infrared pixels. The results produced with this technique are different from those generated using original images which account for a new gateway in the calculation of the NDVI.