Data fusion Grid segment

Kussul, Nataliia; Hluchý, Ladislav; Shelestov, Andrii; Skakun, Sergii; Kravchenko, Oleksii; Ilin, Mykola; Gripich, Yu.; Lavrenyuk, A.

doi:10.15407/knit2009.02.049

Cited by 3 publications

(3 citation statements)

References 9 publications

(7 reference statements)

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“…Both crop masks provide quite good results with very similar statistical properties, while higher accuracy of forecasting could be reached with dynamic crop masks. We plan to implement this approach in our future work with use of high-performance computations (Kussul et al, 2009(Kussul et al, , 2010aKravchenko et al, 2008, Shelestov et al, 2006. The regression model with the best statistical properties is received for county level when satellite based biophysical predictors (FAPAR or LAI) are used.…”

Section: Discussionmentioning

confidence: 99%

Comparison of biophysical and satellite predictors for wheat yield forecasting in Ukraine

Kolotii

Kussul

Shelestov

et al. 2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Winter wheat crop yield forecasting at national, regional and local scales is an extremely important task. This paper aims at assessing the efficiency (in terms of prediction error minimization) of satellite and biophysical model based predictors assimilation into winter wheat crop yield forecasting models at different scales (region, county and field) for one of the regions in central part of Ukraine. Vegetation index NDVI, as well as different biophysical parameters (LAI and fAPAR) derived from satellite data and WOFOST crop growth model are considered as predictors of winter wheat crop yield forecasting model. Due to very short time series of reliable statistics (since 2000) we consider single factor linear regression. It is shown that biophysical parameters (fAPAR and LAI) are more preferable to be used as predictors in crop yield forecasting regression models at each scale. Correspondent models possess much better statistical properties and are more reliable than NDVI based model. The most accurate result in current study has been obtained for LAI values derived from SPOT-VGT (at 1 km resolution) on county level. At field level, a regression model based on satellite derived LAI significantly outperforms the one based on LAI simulated with WOFOST.

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Section: Discussionmentioning

confidence: 99%

Comparison of biophysical and satellite predictors for wheat yield forecasting in Ukraine

Kolotii

Kussul

Shelestov

et al. 2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…However, some other studies show MLP to outperform SVM and DT (Gallego et al, 2012(Gallego et al, , 2014. Though the MLP training phase might be resource and time consuming (but this is becoming less problematic with the use of high-performance computations (Kravchenko et al, 2008;Kussul et al, 2009Kussul et al, , 2010aKussul et al, , 2010bKussul et al, , 2012Shelestov et al, 2006;Shelestov and Kussul, 2008)), and might require experience from the user, it has several advantages over SVM and DT. In particular, MLP is fast at processing new data which can be critical to the processing of large volumes of satellite data, and can produce probabilistic outputs which can be used for indicating reliability of the map.…”

Section: Committee Of Neural Network For Image Classificationmentioning

confidence: 99%

Regional scale crop mapping using multi-temporal satellite imagery

Kussul

Skakun

Shelestov

et al. 2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:One of the problems in dealing with optical images for large territories (more than 10,000 sq. km) is the presence of clouds and shadows that result in having missing values in data sets. In this paper, a new approach to classification of multi-temporal optical satellite imagery with missing data due to clouds and shadows is proposed. First, self-organizing Kohonen maps (SOMs) are used to restore missing pixel values in a time series of satellite imagery. SOMs are trained for each spectral band separately using nonmissing values. Missing values are restored through a special procedure that substitutes input sample's missing components with neuron's weight coefficients. After missing data restoration, a supervised classification is performed for multi-temporal satellite images. An ensemble of neural networks, in particular multilayer perceptrons (MLPs), is proposed. Ensembling of neural networks is done by the technique of average committee, i.e. to calculate the average class probability over classifiers and select the class with the highest average posterior probability for the given input sample. The proposed approach is applied for regional scale crop classification using multi temporal Landsat-8 images for the JECAM test site in Ukraine in 2013. It is shown that ensemble of MLPs provides better performance than a single neural network in terms of overall classification accuracy, kappa coefficient, and producer's and user's accuracies for separate classes. The overall accuracy more than 85% is achieved. The obtained classification map is also validated through estimated crop areas and comparison to official statistics.

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“…They are mainly aimed at solving resource management problems or are focused on the problems of choosing [11] the architecture of a multiprocessor system, or go towards the development of parallel algorithms, resource monitoring and load balancing. This is due to the fact that the efficiency of using distributed systems, in this case, is determined by the possibility of organizing parallel processing [12][13][14].…”

Section: Related Workmentioning

confidence: 99%

Effective Distribution of Tasks in Multiprocessor and Multi-Computers Distributed Homogeneous Systems

Zybin¹,

Khoroshko²,

Maksymovych³

et al. 2021

IJC

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Nowadays, a promising is the direction associated with the use of a large number of processors to solve the resource-intensive tasks. The enormous potential of multiprocessor and multicomputer systems can be fully revealed only when we apply effective methods for organizing the distribution of tasks between processors or computers. However, the problem of efficient distribution of tasks between processors and computers in similar computing systems remains relevant. Two key factors are critical and have an impact on system performance. This is load uniformity and interprocessor or intercomputer interactions. These conflicting factors must be taken into account simultaneously in the distribution of tasks in multiprocessor computing systems. A uniform loading plays a key role in achieving high parallel efficiency, especially in systems with a large number of processors or computers. Efficiency means not only the ability to obtain the result of computations in a finite number of iterations with the necessary accuracy, but also to obtain the result in the shortest possible time. The number of tasks intended for execution on each processor or each computer should be determined so that the execution time is minimal. This study offers a technique that takes into account the workload of computers and intercomputer interactions, and allows one to minimize the execution time of tasks. The technique proposed by the authors allows the comparison of different architectures of computers and computing modules. In this case, a parameter is used that characterizes the behavior of various models with a fixed number of computers, as well as a parameter that is necessary to compare the effectiveness of each computer architecture or computing module when a different number of computers are used. The number of computers can be variable at a fixed workload. The mathematical implementation of this method is based on the problem solution of the mathematical optimization or feasibility.

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Data fusion Grid segment

Cited by 3 publications

References 9 publications

Comparison of biophysical and satellite predictors for wheat yield forecasting in Ukraine

Comparison of biophysical and satellite predictors for wheat yield forecasting in Ukraine

Regional scale crop mapping using multi-temporal satellite imagery

Effective Distribution of Tasks in Multiprocessor and Multi-Computers Distributed Homogeneous Systems

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