Pix2Pix Network to Estimate Agricultural Near Infrared Images from RGB Data

Lima, Daniel Caio de; Saqui, Diego; Mpinda, Steve Ataky Tsham; Saito, José Hiroki

doi:10.1080/07038992.2021.2016056

Cited by 11 publications

(6 citation statements)

References 22 publications

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“…This paper proposes the use of Pix2Pix GANs for crowd image denoising and enhancement prior to passing to the crowd model for density estimation. Pix2Pix GANs have been applied previously in various image translation tasks [18]- [20] with significant improvements in results.…”

Section: Introductionmentioning

confidence: 99%

Revisiting crowd counting: State-of-the-art, trends, and future perspectives

Khan

Menouar

Hamila

2023

Image and Vision Computing

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Section: Introductionmentioning

confidence: 99%

Revisiting crowd counting: State-of-the-art, trends, and future perspectives

Khan

Menouar

Hamila

2023

Image and Vision Computing

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“…GAN, along with the Savitzky–Golay filter, has been used for effective MODIS LAI time series reconstruction 24 . Pix2pix GAN has been used to generate near-infrared band from RGB images acquired by unmanned aerial vehicle much more efficiently than the endmembers method 33 . Time series soil moisture content was estimated using Sentinel-1 images using the CycleGAN model 25 .…”

Section: Related Workmentioning

confidence: 99%

“…However, we achieved PSNR of 29.447 dB without using any data augmentation technique. In the work of RGB to NIR translation, 33 the authors initially converted the RGB images to HSV and CIE lab color spaces and then translated these to NIR images using pix2pix GAN. The RGB images were acquired using a UAV at a lower altitude; hence, they are cloud-free.…”

Section: Comparison Studymentioning

confidence: 99%

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SAR2NDVI: a pix2pix generative adversarial network for reconstructing field-level normalized difference vegetation index time series using Sentinel-1 synthetic aperture radar data

Ramathilagam

Natarajan

Kumar

2023

J. Appl. Rem. Sens.

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The normalized difference vegetation index (NDVI) is essential for monitoring urban green space, forest cover, and crop growth from sowing to harvesting. High-resolution images from Sentinel-2 and Landsat-8 satellites are available nowadays to get field-level phenological information at short intervals. However, cloud cover is one of the biggest hindrances in vegetation monitoring using NDVI, resulting in data gaps. To address this issue, we propose an NDVI gap-filling methodology to generate cloud-free NDVI time series from Sentinel-1 SAR data using the pix2pix generative adversarial network (GAN) model. Pix2pix GANs with generators based on U-Net and ResNet were designed, and the performance of the models was compared for both the VV and VH polarizations. The generalization capability of the models was studied using synthetic aperture radar (SAR) and NDVI image pairs with different vegetation, field sizes, and shapes. Experimental results show that the generated synthetic NDVI images can effectively substitute the cloudy images for gap-filling. Compared to ResNet, the U-Net generator has given the best results with PSNR ¼ 29.447 dB, RMSE ¼ 0.055, and Pearson correlation coefficient ρ ¼ þ0.909 for VH polarization.

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“…These RGB to NIR conversion methods can be utilized for smart plant phenotyping. Some efforts [4], [5] has been done in this direction.…”

Section: Introductionmentioning

confidence: 99%

High-Resolution NIR Prediction from RGB Images: Application to Plant Phenotyping

Shukla

Upadhyay

Sharma

et al. 2022

2022 IEEE International Conference on Image Processing (ICIP)

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In contrast to the conventional RGB cameras, Near-infrared (NIR) spectroscopy provides images with rich information concerning the biological process of plants. However, NIR spectroscopy is a costly affair and produces low-resolution (LR) images. In this context, recently deep learning-based methods have been proposed in computer vision. In addition, the development of phenomics facilities has facilitated the generation of large plant data necessary for the utilization of these deep learning-based methods. Motivated by these developments, we propose a novel attention-based pix-to-pix generative adversarial network (GAN) followed by a superresolution (SR) module to generate high-resolution (HR) NIR images from corresponding RGB images. An experiment including extraction of phenotypic data based on HR NIR images has also been conducted to evaluate its efficacy from an agricultural perspective. Our proposed architecture achieved state-of-the-art performance in terms of MRAE and RMSE on the Wheat plant multi-modality dataset.

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Pix2Pix Network to Estimate Agricultural Near Infrared Images from RGB Data

Cited by 11 publications

References 22 publications

Revisiting crowd counting: State-of-the-art, trends, and future perspectives

Revisiting crowd counting: State-of-the-art, trends, and future perspectives

SAR2NDVI: a pix2pix generative adversarial network for reconstructing field-level normalized difference vegetation index time series using Sentinel-1 synthetic aperture radar data

High-Resolution NIR Prediction from RGB Images: Application to Plant Phenotyping

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