Topographic phase simulation is important for deformation estimation in differential synthetic aperture radar (SAR) interferometry (DInSAR). The most commonly used 30-m resolution SRTM digital elevation model (DEM) is usually required to be resampled due to its relatively low resolution (LR) comparing to the high resolution (HR) SAR images. Although the WorldDEM TM with a 12-m resolution achieves global coverage, it is not available freely. Consequently, it is useful to evaluate the practicability of the super-resolution (SR) from LR SRTM DEMs to HR WorldDEM TM ones, which has not been investigated. Most existing DEM SR models are trained with synthetic datasets in which the LR DEMs are downsampled from their HR counterparts. However, these models become less effective when applied to real-world scenarios due to the domain gap between the synthetic and real LR DEMs. In this paper, we constructed a realworld DEM SR dataset where the LR and HR DEMs were collected from SRTM and WorldDEM TM , respectively. An ESRGAN model was adapted to train on the dataset. Considering that the real LR-HR pairs may suffer from misalignment, we introduced the perceptual loss for better optimizing the model. Moreover, a logarithmic normalization was proposed to compress the wide elevation range and adjust the uneven distribution.We also pretrained the model using natural images since collecting sufficient HR DEMs is costly. Experiments demonstrate that the proposed method achieves near 0.69dB improvement of peak signal-to-noise ratio (PSNR). In addition, our method is also validated to improve the topographic phase simulation by 23.42% of MSE.
Various perishable agricultural products are recalled due to harmful health risks. Blockchain has been used to reduce the amount of such products wasted and disposed. Specifically, a supply chain with a wholesaler, a retailer, and customers is considered where the retailer decides when to switch from a conventional supply chain information management system (SCIMS) to a blockchain-based SCIMS. This article models the uncertain customers' demand as a geometric Brownian motion process and shows how to obtain the optimal demand threshold above which the switch occurs and the corresponding expected time. Next, the model is extended by incorporating two types of government subsidies (i.e., a fixed subsidy on the switching cost and a variable subsidy per unit demand). Through sensitivity analysis and numerical studies, the impacts of key parameters on the optimal demand threshold and expected time of switching are presented. Finally, managerial insights and policy implications are derived.
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