10 research outputs found
Grid Approach to Satellite Monitoring Systems Integration
This paper highlights the challenges of satellite monitoring systems integration, in particular based on
Grid platform, and reviews possible solutions for these problems. We describe integration issues on different
levels: data integration level and task management level (job submission in terms of Grid). We show example of
described technologies for integration of monitoring systems of Ukraine (National Space Agency of Ukraine,
NASU) and Russia (Space Research Institute RAS, IKI RAN). Another example refers to the development of
InterGrid infrastructure that integrates several regional and national Grid systems: Ukrainian Academician Grid
(with Satellite data processing Grid segment) and RSGS Grid (Chinese Academy of Sciences)
Intelligent Model of User Behavior in Distributed Systems
We present a complex neural network model of user behavior in distributed systems. The model
reflects both dynamical and statistical features of user behavior and consists of three components: on-line and
off-line models and change detection module. On-line model reflects dynamical features by predicting user
actions on the basis of previous ones. Off-line model is based on the analysis of statistical parameters of user
behavior. In both cases neural networks are used to reveal uncharacteristic activity of users. Change detection
module is intended for trends analysis in user behavior. The efficiency of complex model is verified on real data of
users of Space Research Institute of NASU-NSAU
Intelligent Computations for Flood Monitoring
Floods represent the most devastating natural hazards in the world, affecting more people and causing
more property damage than any other natural phenomena. One of the important problems associated with flood
monitoring is flood extent extraction from satellite imagery, since it is impractical to acquire the flood area through
field observations. This paper presents a method to flood extent extraction from synthetic-aperture radar (SAR)
images that is based on intelligent computations. In particular, we apply artificial neural networks, self-organizing
Kohonenβs maps (SOMs), for SAR image segmentation and classification. We tested our approach to process
data from three different satellite sensors: ERS-2/SAR (during flooding on Tisza river, Ukraine and Hungary,
2001), ENVISAT/ASAR WSM (Wide Swath Mode) and RADARSAT-1 (during flooding on Huaihe river, China,
2007). Obtained results showed the efficiency of our approach
Data Assimilation Technique For Flood Monitoring and Prediction
This paper focuses on the development of methods and cascade of models for flood monitoring and
forecasting and its implementation in Grid environment. The processing of satellite data for flood extent mapping
is done using neural networks. For flood forecasting we use cascade of models: regional numerical weather
prediction (NWP) model, hydrological model and hydraulic model. Implementation of developed methods and
models in the Grid infrastructure and related projects are discussed
Grid Infrastructure for Satellite Data Processing in Ukraine
In this paper conceptual foundations for the development of Grid systems that aimed for satellite data
processing are discussed. The state of the art of development of such Grid systems is analyzed, and a model of
Grid system for satellite data processing is proposed. An experience obtained within the development of the Grid
system for satellite data processing in the Space Research Institute of NASU-NSAU is discussed
Π‘ΡΠ°Π²Π½ΠΈΡΠ΅Π»ΡΠ½ΡΠΉ Π°Π½Π°Π»ΠΈΠ· Π½Π΅ΠΉΡΠΎΠ½Π½ΡΡ ΡΠ΅ΡΠ΅ΠΉ ΠΈ ΡΡΠ°ΡΠΈΡΡΠΈΡΠ΅ΡΠΊΠΈΡ ΠΏΠΎΠ΄Ρ ΠΎΠ΄ΠΎΠ² ΠΊ ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ Π΄ΠΈΡΡΠ°Π½ΡΠΈΠΎΠ½Π½ΠΎΠ³ΠΎ Π·ΠΎΠ½Π΄ΠΈΡΠΎΠ²Π°Π½ΠΈΡ
ΠΡΡΡΡΠ»Ρ ΠΠ°ΡΠ°Π»ΡΡ, Π‘ΠΊΠ°ΠΊΡΠ½ Π‘Π΅ΡΠ³Π΅ΠΉ, ΠΡΡΡΡΠ»Ρ ΠΠ»ΡΠ³Π°. Π‘ΡΠ°Π²Π½ΠΈΡΠ΅Π»ΡΠ½ΡΠΉ Π°Π½Π°Π»ΠΈΠ· Π½Π΅ΠΉΡΠΎΠ½Π½ΡΡ
ΡΠ΅ΡΠ΅ΠΉ ΠΈ ΡΡΠ°ΡΠΈΡΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΏΠΎΠ΄Ρ
ΠΎΠ΄ΠΎΠ² ΠΊ ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ Π΄ΠΈΡΡΠ°Π½ΡΠΈΠΎΠ½Π½ΠΎΠ³ΠΎ Π·ΠΎΠ½Π΄ΠΈΡΠΎΠ²Π°Π½ΠΈΡThis paper examines different approaches to remote sensing images classification. Included in the study are statistical approach, namely Gaussian maximum likelihood classifier, and two different neural networks paradigms: multilayer pcreeptron trained with EDBD algorithm, and ARTMAP neural network. These classification methods are compared on data acquired from Landsat-7 satellite. Experimental results showed that to achieve better performance of classifiers modular neural networks and committee machines should be applied