<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Generative adversarial networks (GAN) opened new possibilities for image processing and analysis. Inpainting, dataset augmentation using artificial samples, or increasing spatial resolution of aerial imagery are only a few notable examples of utilising GANs in remote sensing (RS). The normalised difference vegetation index (NDVI) ground-truth labels were prepared by combining RGB and NIR orthophotos. The dataset was then utilised as input for a conditional generative adversarial network (cGAN) to perform an image-to-image translation. The main goal of the neural network was to generate an artificial NDVI image for each processed 256 px × 256 px patch using only information available in the panchromatic input. The network achieved a structural similarity index measure (SSIM) of 0.7569 ± 0.1083, a peak signal-to-noise ratio (PSNR) of 26.6459 ± 3.6577 and a root-mean-square error (RSME) of 0.0504 ± 0.0193 on the test set, which should be considered high. The perceptual evaluation was performed to verify the method's usability when working with a real-life scenario. The research confirms that the structure and texture of the panchromatic aerial RS image contain sufficient information for NDVI estimation for various objects of urban space. Even though these results can highlight areas rich in vegetation and distinguish them from the urban background, there is still room for improvement regarding the accuracy of the estimated values. The research aims to explore the possibility of utilising GAN to enhance panchromatic images (PAN) with information related to vegetation. This opens exciting opportunities for historical RS imagery processing and analysis.</p>
</abstract>

Generative adversarial networks (GAN) opened new possibilities for image processing and analysis. Inpainting, dataset augmentation using artificial samples, or increasing spatial resolution of aerial imagery are only a few notable examples of utilising GANs in remote sensing (RS). The normalised difference vegetation index (NDVI) ground-truth labels were prepared by combining RGB and NIR orthophotos. The dataset was then utilised as input for a conditional generative adversarial network (cGAN) to perform an image-to-image translation. The main goal of the neural network was to generate an artificial NDVI image for each processed 256 px × 256 px patch using only information available in the panchromatic input. The network achieved a structural similarity index measure (SSIM) of 0.7569 ± 0.1083, a peak signal-to-noise ratio (PSNR) of 26.6459 ± 3.6577 and a root-mean-square error (RSME) of 0.0504 ± 0.0193 on the test set, which should be considered high. The perceptual evaluation was performed to verify the method's usability when working with a real-life scenario. The research confirms that the structure and texture of the panchromatic aerial RS image contain sufficient information for NDVI estimation for various objects of urban space. Even though these results can highlight areas rich in vegetation and distinguish them from the urban background, there is still room for improvement regarding the accuracy of the estimated values. The research aims to explore the possibility of utilising GAN to enhance panchromatic images (PAN) with information related to vegetation. This opens exciting opportunities for historical RS imagery processing and analysis.

Generative Adversarial Approach to Urban Areas’ NDVI Estimation: A Case Study of Łódź, Poland

Quaestiones Geographicae

This work is licensed under the Creative Commons Attribution 4.0 International License.

Generative adversarial networks (GAN) opened new possibilities for image processing and analysis. Inpainting, dataset augmentation using artificial samples, or...

Generative Adversarial Approach to Urban Areas’ NDVI Estimation: A Case Study of Łódź, Poland

Pubblicato online: 29 gen 2023

Pagine: 87 - 106

Ricevuto: 27 set 2022

DOI: https://doi.org/10.14746/quageo-2023-0007

Parole chiave
generative adversarial networks, NDVI, green areas, orthophoto, artificial datasets

© 2023 Maciej Adamiak et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Generative Adversarial Approach to Urban Areas’ NDVI Estimation: A Case Study of Łódź, Poland

Pubblicato online: 29 gen 2023

Pagine: 87 - 106

Ricevuto: 27 set 2022

DOI: https://doi.org/10.14746/quageo-2023-0007

Parole chiavegenerative adversarial networks, NDVI, green areas, orthophoto, artificial datasets

© 2023 Maciej Adamiak et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Parole chiave
generative adversarial networks, NDVI, green areas, orthophoto, artificial datasets