RESUMEN
In this article, we present a new pansharpening method, a zero-reference generative adversarial network (ZeRGAN), which fuses low spatial resolution multispectral (LR MS) and high spatial resolution panchromatic (PAN) images. In the proposed method, zero-reference indicates that it does not require paired reduced-scale images or unpaired full-scale images for training. To obtain accurate fusion results, we establish an adversarial game between a set of multiscale generators and their corresponding discriminators. Through multiscale generators, the fused high spatial resolution MS (HR MS) images are progressively produced from LR MS and PAN images, while the discriminators aim to distinguish the differences of spatial information between the HR MS images and the PAN images. In other words, the HR MS images are generated from LR MS and PAN images after the optimization of ZeRGAN. Furthermore, we construct a nonreference loss function, including an adversarial loss, spatial and spectral reconstruction losses, a spatial enhancement loss, and an average constancy loss. Through the minimization of the total loss, the spatial details in the HR MS images can be enhanced efficiently. Extensive experiments are implemented on datasets acquired by different satellites. The results demonstrate that the effectiveness of the proposed method compared with the state-of-the-art methods. The source code is publicly available at https://github.com/RSMagneto/ZeRGAN.
RESUMEN
Recently, deep learning based multispectral (MS) and panchromatic (PAN) image fusion methods have been proposed, which extracted features automatically and hierarchically by a series of non-linear transformations to model the complicated imaging discrepancy. But they always pay more attention to the extraction and compensation of spatial details and use the mean squared error or mean absolute error as a loss function, regardless of the preservation of spectral information contained in multispectral images. For the sake of the improvements in both spatial and spectral resolution, this paper presents a novel fusion model that takes the spectral preservation into consideration, and learns the spectral cues from the process of generating a spectrally refined multispectral image, which is constrained by a spectral loss between the generated image and the reference image. Then these spectral cues are used to modulate the PAN features to obtain final fusion result. Experimental results on reduced-resolution and full-resolution datasets demonstrate that the proposed method can obtain a better fusion result in terms of visual inspection and evaluation indices when compared with current state-of-the-art methods.
RESUMEN
The fungal species Fusarium can cause devastating disease in agricultural crops. Phenamacril is an extremely specific cyanoacrylate fungicide and a strobilurine analog that has excellent efficacy against Fusarium. To date, information on the mechanisms involved in the uptake, accumulation, and metabolism of phenamacril in plants is scarce. In this study, lettuce and radish were chosen as model plants for a comparative analysis of the absorption, accumulation, and metabolic characteristics of phenamacril from a polluted environment. We determined the total amount of phenamacril in the plant-water system by measuring the concentrations in the solution and plant tissues at frequent intervals over the exposure period. Phenamacril was readily taken up by the plant roots with average root concentration factor ranges of 60.8-172.7 and 16.4-26.9 mL/g for lettuce and radish, respectively. However, it showed limited root-to-shoot translocation. The lettuce roots had a 2.8-12.4-fold higher phenamacril content than the shoots; whereas the radish plants demonstrated the opposite, with the shoots having 1.5 to 10.0 times more phenamacril than the roots. By the end of the exposure period, the mass losses from the plant-water systems reached 72.0% and 66.3% for phenamacril in lettuce and radish, respectively, suggesting evidence of phenamacril biotransformation. Further analysis confirmed that phenamacril was metabolized via hydroxylation, hydrolysis of esters, demethylation, and desaturation reactions, and formed multiple transformation products. This study furthers our understanding of the fate of phenamacril when it passes from the environment to plants and provides an important reference for its scientific use and risk assessment.