Moiré patterns, appearing as color distortions, severely degrade image and video qualities when filming a screen with digital cameras. Considering the increasing demands for capturing videos, we study how to remove such undesirable moiré patterns in videos, namely video demoiréing. To this end, we introduce the first hand-held video demoiréing dataset with a dedicated data collection pipeline to ensure spatial and temporal alignments of captured data. Further, a baseline video demoiréing model with implicit feature space alignment and selective feature aggregation is developed to leverage complementary information from nearby frames to improve frame-level video demoiréing. More importantly, we propose a relation-based temporal consistency loss to encourage the model to learn temporal consistency priors directly from ground-truth reference videos, which facilitates producing temporally consistent predictions and effectively maintains frame-level qualities. Extensive experiments manifest the superiority of our model. Code is available at ht tps:// daipengwa. github.io/VDmoire_ProjectPage/.
Abstract. The AHS (Airborne Hyperspectral Scanner) instrument has 80 spectral bands covering the visible and near infrared (VNIR), short wave infrared (SWIR), mid infrared (MIR) and thermal infrared (TIR) spectral range. The instrument is operated by Instituto Nacional de Técnica Aerospacial (INTA), and it has been involved in several field campaigns since 2004. This paper presents an overview of the work performed with the AHS thermal imagery provided in the framework of the SPARC and SEN2FLEX campaigns, carried out respectively in 2004 and 2005 over an agricultural area in Spain. The data collected in both campaigns allowed for the first time the development and testing of algorithms for land surface temperature and emissivity retrieval as well as the estimation of evapotranspiration from AHS data. Errors were found to be around 1.5 K for land surface temperature and 1 mm/day for evapotranspiration.
We develop a numerical method for a geometric dynamic anisotropic damage model. The coupled phenomena analyzed here deal with a loading wave, which damages the material and changes the propagation properties of the material. In this way the damage processes induced by it perturbs the speed and the profile of the loading wave. The geometric damage model, represented by micro-cracks growing under dynamical loading, is able to describe the link between the micro and macro-scale characteristic times and the rate of deformation. The micro-crack growth is activated in some privileged directions according to the applied macroscopic loads and the velocity of the micro-crack propagation is estimated by the dynamic stress intensity factor. A discontinuous Galerkin numerical scheme for the numerical integration of the damage model is also proposed. The scheme is robust and precise. Several two-dimensional boundary value problems are selected to illustrate the model and to analyze the robustness of the numerical algorithm.
Abstract. A discrete rainfall–runoff model has been developed, which uses retrievals of Water Saturated Soil (WSS) and inundation area from 37 GHz microwave observations. The model was implemented at three levels of increasing complexity using field-measured ground water table, WSS and inundated area, and precipitation data. The three levels, defined by the key-variables are: (1) precipitation and base flow; (2) overland flow, infiltrated flow and base flow; (3) overland flow, potential subsurface flow and base flow. The base flow is estimated from observed ground water table depth, while overland and infiltrated flows are estimated from precipitation and the WSS and inundated area. A linear scaling method is developed to estimate the potential subsurface flow. The three model implementations are calibrated with the gauge measurements of 10-day average river discharge in 2002 and 2005 respectively at Changsha station, downstream of Xiangjiang River basin, China. The discrete rainfall–runoff model assumes that specific runoff is determined by antecedent precipitations over a variable period of time. This duration is a model parameter varying between 10 and 150 days. The performance of the discrete rainfall–runoff model increased with the duration of antecedent precipitation for all three implementations in both years. With a duration of 150 days, the model reaches its best performance: Nash–Sutcliffe Efficiency, NSE, for the 1st implementation was ≥ 0.90 with relative RMSE ≤ 22 %; NSE ≈ 0.99 with relative RMSE ≤ 5 % for the 2nd implementation, and NSE ≥ 0.99 with relative RMSE ≤ 4 % for the 3rd one. These good performances prove that the retrievals of WSS and inundated area clearly improve model accuracy, thus justifying the choices of parameters and the method to estimate the potential subsurface flow. The set of parameters driving each implementation is an indication of dominant hydrological processes, particularly water storage, in determining the catchment response to rainfall. Significant differences in the annual water yield have been observed across the three implementations. The relative RMSE in each season demonstrates the possible recharge period of the ground water in Xiangjiang River basin.
The discrete distribution clustering algorithm, namely D2-clustering, has demonstrated its usefulness in image classification and annotation where each object is represented by a bag of weighed vectors. The high computational complexity of the algorithm, however, limits its applications to large-scale problems. We present a parallel D2-clustering algorithm with substantially improved scalability. A hierarchical structure for parallel computing is devised to achieve a balance between the individual-node computation and the integration process of the algorithm. Additionally, it is shown that even with a single CPU, the hierarchical structure results in significant speed-up. Experiments on real-world large-scale image data, Youtube video data, and protein sequence data demonstrate the efficiency and wide applicability of the parallel D2-clustering algorithm. The loss in clustering accuracy is minor in comparison with the original sequential algorithm.
An exceptional drought struck Henan province during the summer of 2014. It caused directly the financial loss reaching to hundreds of billion Yuan (RMB), and brought the adverse influence for people's life, agricultural production as well as the ecosystem. The study in this paper characterized the Henan 2014 summer drought event through analyzing the spatial distribution of drought severity using precipitation data from Tropical Rainfall Measuring Mission (TRMM) sensor and Normalized difference vegetation index (NDVI) and land surface temperature (LST) products from Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. The trend analysis of the annual precipitation from 2003 to 2014 showed that the region over Henan province is becoming dry. Especially in the east of Henan province, the decrease of precipitation is more obvious with the maximum change rate of ~48 mm/year. The rainfall in summer (from June to August) of 2014 was the largest negtive anomaly in contrast with the same period of historical years, which was 43% lower than the average of the past ten years. Drought severity derived from Standardized Precipitation Index (SPI) indicated that all areas of Henan province experienced drought in summer of 2014 with different severity levels. The extreme drought, accounting for about 22.7 % of Henan total area, mainly occurred in Luohe, Xuchang, and Pingdingshan regions, and partly in Nanyang, Zhengzhou, and Jiaozuo. This is consistent with the statistics from local municipalities. The Normalized Drought Index Anomaly (NDAI), calculated from MODIS NDVI and LST products, can capture the evolution of the Henan 2014 summer drought effectively. Drought severity classified by NDAI also agreed well with the result from the SPI.
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