In order to insure the watermarking embedding merits,such as high speed,easy realization,etc.and improve its safeness and robustness,a meaning binary image is encrypted quickly based on chaos theory because of chaos system merits.Then the wavelet coefficients are arrayed by their importance and the most important number L coefficients,the part which is most important to the human vision,is selected as the spot which should be embedded by watermarking.At last the watermarking is embedded in the coefficient bit plane and the carrier image containing the watermarking is acquired by the retransform.The experimental result proves that this algorithm has merits such as high efficiency,strong ability to resist compression,better robustness,etc.
This paper presents a multi-scale Best-Buddies Similarity (BBS) method for matching objects in the UAV imagery. More precisely, we first extract proposed regions from the target image by Selective Search, then take a number of proposed regions with the highest confidence scores as templates and finally apply templates to the original BBS method and select the coordinate of the region with the highest overlap rate as the exact position of the query object in the target image. Experimental results on real data show the effectiveness of the proposed method.
As a major world crop, the accurate spatial distribution of winter wheat is important for improving planting strategy and ensuring food security. Due to big data management and processing requirements, winter wheat mapping based on remote-sensing data cannot ensure a good balance between the spatial scale and map details. This study proposes a rapid and robust phenology-based method named “enhanced time-weighted dynamic time warping” (E-TWDTW), based on the Google Earth Engine, to map winter wheat in a finer spatial resolution, and efficiently complete the map of winter wheat at a 10-m resolution in Henan Province, China. The overall accuracy and Kappa coefficient of the resulting map are 97.98% and 0.9469, respectively, demonstrating its great applicability for winter wheat mapping. This research indicates that the proposed approach is effective for mapping large-scale planting patterns. Furthermore, based on comparative experiments, the E-TWDTW method has shown excellent robustness across lower quantities of training data and early season extraction ability. Therefore, it can provide early data preparation for winter wheat planting management in the early stage.
Low Earth Orbit (LEO) satellite constellations have seen significant growth and functional enhancement in recent years, which integrates various capabilities like communication, navigation, and remote sensing. However, the heterogeneity of data collected by different satellites and the problems of efficient inter-satellite collaborative computation pose significant obstacles to realizing the potential of these constellations. Existing approaches struggle with data heterogeneity, varing image resolutions, and the need for efficient on-orbit model training. To address these challenges, we propose a novel decentralized PFL framework, namely, A Novel Decentra L ized Person A lized Federated Learning for Heteroge N eous LEO Satell I te Co N st E llation (ALANINE). ALANINE incorporates decentralized FL (DFL) for satellite image Super Resolution (SR), which enhances input data quality. Then it utilizes PFL to implement a personalized approach that accounts for unique characteristics of satellite data. In addition, the framework employs advanced model pruning to optimize model complexity and transmission efficiency. The framework enables efficient data acquisition and processing while improving the accuracy of PFL image processing models. Simulation results demonstrate that ALANINE exhibits superior performance in on-orbit training of SR and PFL image processing models compared to traditional centralized approaches. This novel method shows significant improvements in data acquisition efficiency, process accuracy, and model adaptability to local satellite conditions.
Abstract. Precipitation forecasting is an important mission in weather science. In recent years, data-driven precipitation forecasting techniques could complement numerical prediction, such as precipitation nowcasting, monthly precipitation projection and extreme precipitation event identification. In data-driven precipitation forecasting, the predictive uncertainty arises mainly from data and model uncertainties. Current deep learning forecasting methods could model the parametric uncertainty by random sampling from the parameters. However, the data uncertainty is usually ignored in the forecasting process and the derivation of predictive uncertainty is incomplete. In this study, the input data uncertainty, target data uncertainty and model uncertainty are jointly modeled in a deep learning precipitation forecasting framework to estimate the predictive uncertainty. Specifically, the data uncertainty is estimated a priori and the input uncertainty is propagated forward through model weights according to the law of error propagation. The model uncertainty is considered by sampling from the parameters and is coupled with input and target data uncertainties in the objective function during the training process. Finally, the predictive uncertainty is produced by propagating the input uncertainty in the testing process. The experimental results indicate that the proposed joint uncertainty modeling framework for precipitation forecasting exhibits better forecasting accuracy (improving RMSE by 1 %–2 % and R2 by 1 %–7 % on average) relative to several existing methods, and could reduce the predictive uncertainty by ∼28 % relative to the approach of Loquercio et al. (2020). The incorporation of data uncertainty in the objective function changes the distributions of model weights of the forecasting model and the proposed method can slightly smooth the model weights, leading to the reduction of predictive uncertainty relative to the method of Loquercio et al. (2020). The predictive accuracy is improved in the proposed method by incorporating the target data uncertainty and reducing the forecasting error of extreme precipitation. The developed joint uncertainty modeling method can be regarded as a general uncertainty modeling approach to estimate predictive uncertainty from data and model in forecasting applications.
This paper presents the algorithms to locate license plate and recognize the characters on it. These algorithms have three advantages. First, they have strong robustness to against many noises and disturbances. Second, the methods can deal with license plates with different colors. Third, the recognition methods based on artificial neural network are suitable for Chinese characters.
Due to natural conditions, work environment, maintenance management and other factors, in the course of the use, the aqueduct hydraulic structures may appear material aging, structural damage, performance and other situations, and bring hidden dangers to the safe operation.Aimed at the insufficient of the research status and the existing problems, according to structure characteristics of the aqueduct, combined with theoretical research and development trend, the paper proposed a method of fuzzy comprehensive evaluation analytic hierarchy to carry out the aqueduct hydraulic structures safety assessment.The method divided the aqueduct hydraulic buildings into three level indicators and four layers.Analytic hierarchy process is used in the weights determine of many influence factors, fuzzy comprehensive evaluation method is used in the multistage evaluation of the many influence factors, and finally with an example to show how the method was used in practical application.
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)