Study of Selected Textural Features Properties on Asbestos Roof Images

Książek, Judyta

doi:10.7494/geom.2018.12.4.45

Cited by 2 publications

(1 citation statement)

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“…The obtained results were also influenced by a certain property of the textural features, namely the sensitivity to changes in some image parameters. As reported by Książek [68], changes in contrast, brightness, sharpness and image rotation may have an impact on several groups of textural features of roofs selected by her for research (asbestos roofs were dealt with). The obtained results were also analyzed in terms of parameters insensitive to image transformations, but no texture features were found that would be resistant to all factors causing their change.…”

Section: Discussionmentioning

confidence: 90%

Generative Adversarial Approach to Urban Areas NDVI Estimation Using Information Exclusively from Structural and Textural Analysis of Panchromatic Orthoimagery: A Case Study of Łódź, Poland

Adamiak¹,

Będkowski²,

Bielecki³

2022

Preprint

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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 utilizing GANs in remote sensing. This is due to a unique construction and training process expressed as a duel between GAN components. The main objective of the research is to apply GAN to generate an artificial Normalized Difference Vegetation Index (NDVI) using panchromatic images. The NDVI ground-truth labels were prepared by combining RGB and NIR orthophoto. The dataset was then utilized 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 256px × 256px patch using only information available in the panchromatic input. The network achieved 0.7569 ± 0.1083 Structural Similarity Index Measure (SSIM), 26.6459 ± 3.6577 Peak Signal-to-Noise Ratio (PNSR) and 0.0504 ± 0.0193 Root-Mean-Square Error (RSME) on the test set. The perceptual evaluation was performed to verify the usability of the method when working with a real-life scenario. The research confirms that the structure and texture of the panchromatic aerial remote sensing image contains sufficient information for NDVI estimation for various objects of urban space. Even though these results can be used to highlight areas rich in vegetation and distinguish them from urban background, there is still room for improvement in terms of accuracy of estimated values. The purpose of the research is to explore the possibility of utilizing GAN to enhance panchromatic images (PAN) with information related to vegetation. This opens interesting possibilities in terms of historical remote sensing imagery processing and analysis. The panchromatic orthoimagery dataset was derived from RGB orthoimagery.

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Section: Discussionmentioning

confidence: 90%

Generative Adversarial Approach to Urban Areas NDVI Estimation Using Information Exclusively from Structural and Textural Analysis of Panchromatic Orthoimagery: A Case Study of Łódź, Poland

Adamiak¹,

Będkowski²,

Bielecki³

2022

Preprint

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Generative adversarial approach to urban areas NDVI estimation: A case study of Łódź, Poland

Adamiak,

Będkowski,

Bielecki

2023

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Generative adversarial networks (GAN) opened new possibilities for image processing and analysis. In- painting, 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 in- formation 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 meth- od’s usability when working with a real-life scenario. The research confirms that the structure and texture of the pan- chromatic 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.

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Study of Selected Textural Features Properties on Asbestos Roof Images

Cited by 2 publications

References 10 publications

Generative Adversarial Approach to Urban Areas NDVI Estimation Using Information Exclusively from Structural and Textural Analysis of Panchromatic Orthoimagery: A Case Study of Łódź, Poland

Generative Adversarial Approach to Urban Areas NDVI Estimation Using Information Exclusively from Structural and Textural Analysis of Panchromatic Orthoimagery: A Case Study of Łódź, Poland

Generative adversarial approach to urban areas NDVI estimation: A case study of Łódź, Poland

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