Despeckling of Multitemporal Sentinel SAR Images and Its Impact on Agricultural Area Classification
Vladimir Lukin, Oleksii Rubel, Ruslan Kozhemiakin, Sergey Abramov, Andrii Shelestov, Mykola Lavreniuk, Mykola Meretsky, Benoit Vozel and Kacem Chehdi
In Recent Advances and Applications in Remote Sensing. IntechOpen (Chapter 2). – 2018. – P. 21-40.

This chapter addresses an important practical task of classification of multichannel remote sensing data with application to multitemporal dual-polarization Sentinel radar images acquired for agricultural regions in Ukraine. We first consider characteristics of dual-polarization Sentinel radar images and discuss what kind of filters can be applied to such data. Several examples of denoising are presented with analysis of what properties of filters are desired and what can be provided in practice. It is also demonstrated that the use of preliminary denoising produces improvement of classification accuracy where despeckling that is more efficient in terms of standard filtering criteria results in better classification.


ImageAutomated information technology for land cover mapping based on satellite data fusion methods and models
Yailymov Bohdan
Supervisor: Professor of the Department of Information Security in the National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, leading scientist of the Space Research Institute NAS Ukraine and SSA Ukraine, PhD, prof. Andrii Shelestov

The dissertation is devoted to solving a problem of accuracy improvement of classification and timely evaluation of area classes of the land cover and support of high accuracy in mapping of the earth’s surface on large areas by developing of the methods of fusion of different types of large volumes of geospatial data and creating of automated mapping technology. The method of classification of the land cover using satellite data of large volumes on the basis of the use of the ensemble of the neural networks was improved and the methods of fusion of different satellite data on pixels’ levels and decisions levels for mapping were implemented in automated information technology for Ukraine within a service-oriented approach. The method of mapping of the land cover was used in obtaining maps for the territory of Ukraine based on Landsat-4/5/7 for 2010, 2000 and 1990. The obtained maps made it possible to assess the general trends of various purpose of the land cover in Ukraine. The problem of the breach of crop rotation based on the developed technology was solved, the land cover maps were developed for the territory of Kyiv region for 2013-2015 years, the problem of assessing of the damages from drought was solved.


ImageDisaster risk analysis based on satellite data. Models and technologies 
Kussul N., Skakun S., Shelestov A.
Kyiv, “Naukova Dumka” – 2014. – 184 p. (in Russian)

The work is dedicated the method preparation and technologies for geospatial risks analysis of natural disasters. It is proposed a problem statement of geospatial risks assessment, related to natural disasters, and demonstrated the methodology of solution using diverse information (satellite and ground data, data modeling). The stages of problem solving and the ensemble approach for the data processing are justified. Examples of practical application of the developed methods and information technologies to risk estimate for floods and droughts. For researchers engaged disaster risk modeling and elimination of the consequences of this disasters, teachers and high school students of relevant specialties.


ImageGeospatial analysis of disaster risk 
Kussul N., Skakun S., Shelestov A.
Kyiv, “Naukova Dumka” – 2014. – 258 p. (in Ukrainian)





ImageGrid and Cloud Database Management Grid 
In Fiore, S.; Aloisio, G. (Eds.). — 2011, Springer.
Kussul N., Shelestov A., Skakun S. / Technologies for Satellite Data Processing and Management Within International Disaster Monitoring Projects. Р. 279-306.




ImageIntelligent Data Processing in Global Monitoring for Environment and Security
ITHEA, Кiev-Sofia, 2011.
Kussul N., Shelestov A., Skakun S., Kravchenko O. / High-performance Intelligent Computations for Environmental and Disaster Monitoring. P. 76-103

This collective scientific monograph is aimed to present several important aspects of Intelligent Data Processing in Global Monitoring for Environment and Security, which are investigated by the authors. The implementing of the results in the corresponded program systems for intelligent data processing in GMES is outlined. It is represented that book chapters will be interesting for experts in the field of intelligent technologies for global observation as well as for practical users.


ImageUse of Satellite and In-Situ Data to Improve Sustainability
F. Kogan, A. Powell, O. Fedorov (Eds.). – NATO Science for Peace and Security Series C: Environmental Security, Springer, 2011.
Kussul N., Shelestov A., Skakun S. / Flood Monitoring on the Basis of SAR Data. P.19-29

This paper presents the intelligent techniques approach for flood ­monitoring using Synthetic Aperture Radar (SAR) satellite images. We applied ­artificial neural networks and Self-Organizing Kohonen Maps (SOMs), to SAR image segmentation and classification. Our approach was used to process data from ­different SAR satellite instruments (ERS-2/SAR, ENVISAT/ASAR, RADARSAT-1/2) for different flood events: Tisza River, Ukraine and Hungary in 2001; Huaihe River, China in 2007; Mekong River, Thailand and Laos in 2008; Koshi River, India and Nepal in 2008; Norman River, Australia in 2009; Lake Liambezi, Namibia in 2009; Mekong River, Laos in 2009. This approach was implemented using Sensor Web paradigm for integrated system for flood monitoring and management.


ImageCollective monograph “State and perspectives of computer science in Ukraine”
Kussul N., Shelestov A. / Optimization of the difficult distributed Grid-system. P.510-517
Kyiv, “Naukova Dumka”, 2010. (in Russian)
The main purpose of the monograph is to acquaint the scientific community to the achievements of Ukrainian scientists that represent different areas of research and development of both fundamental and applied importance. Along with the attempt to capture the main achievements for the last 50 years the authors focused on the prospects for future development of science, and information society in particular . For scientists and technical staff dealing with informatics and cybernetics, and for teachers and students as well.



ImageGrid Systems for Earth Observations. Architecture, models and technologies
Kussul N., Shelestov A.
Kyiv, “Naukova Dumka”, 2008. – 452 p. (in Russian)

Monograph is dedicated to the questions of Grid-systems modeling and creation with respect to the specifics of applied tasks of Earth observation. The architecture, methods of modeling and technologies of creation of modern systems are taken apart. The authors introduce the integrated system approach to analysis and modeling of such systems on different stages of life-cycle. All of the proposed approaches have been successfully approved during the creation of international level real systems.
Monograph of high interest both for experienced specialists in the field of informational technologies and for young scientists, just starting their career. For the experienced specialist the new results and the analysis of known approaches to creation and modeling of such systems are represented in the book. The book will help the newbie to learn the state of art of the research in this field



Intelligent Computations for Earth Observation Data Processing
Kussul N., Shelestov A., Skakun S., Kravchenko A.
Kyiv, “Naukova Dumka”, 2007. – 196 p. (in Russian)

Monograph is dedicated to modern tasks and methods of Earth observation data processing. The usage of intelligent computations and data integration methods for solving numerous tasks are taken apart. In particular, monitoring and prediction of floods, assessment of the vegetation state and biodiversity, meteorological parameters gathering using the satellite and modeling data, structure and parametrical identification of complex models, space weather parameters prediction. All scientific thesis’s are illustrated by the results of computer modeling
The book is designed for professionals in intelligent computations, mathematical modeling and satellite data processing



Intelligent Computations (approved by Ministry of Education and Science of Ukraine)
Kussul N.M, Shelestov A.Yu., Lavrenyuk A.M.
Kyiv, “Naukova Dumka”, 2006. — 186 p. (in Ukrainian)

In this educational book the authors were trying to systematize intelligent computations theory, its main concepts, heuristics and creating algorithms. The book is designed for senior students with basic knowledge of mathematics and computer sciences studying Computer Sciences and Applied Mathematics.



ImageApplication of РНР
Kussul N.N., Shelestov A.Yu.
Moscow: Williams, 2005.





Programming. Fundamentals of Web Applications Development (for 2nd year students of Physics & Technology Institute of NTUU “KPI”) 
Kussul N.M, Shelestov A.Yu., Lavrenyuk A.M., Skakun S.V.
Kyiv: «Polytechnika», 2005. – 52 p. (in Ukrainian)

ImageRobust Methods for Estimation, Identification and Adaptive Control
Azarskov V.M., Blokhyn L.N., Zhytetski L.S., Kussul N.N.
Кyiv, National Aviation University, 2004. — 500 p. (in Russian)





Programming. Object-oriented approach (for 1st year students of Physics & Technology Institute of NTUU “KPI”)
Kussul N.M, Lavrenyuk A.M., Shelestov A.Yu. Timoschyk O.L.
Кyiv, Politechnika, 2003. – 48 p. (in Ukrainian)

“Programming and algorithmical languages”
Kussul N.M, Timoschuk O.L., Shelestov A.Yu.
Kiev: NTUU “KPI”, 2000. – 49 p. (in Ukrainian)

“Programming. С++ Fundamentals”
Kussul N.M, Timoschuk O.L., Shelestov A.Yu.
Kiev: PTI NTUU “KPI”, 2000. – 52 p. (in Ukrainian)

“Fundamentals of Computer Software”
Kussul N.M, Luntovsky A.O.
Kiev: PTI NTUU “KPI”, 2000. – 84 p. (in Ukrainian)

“Computer Systems Fundamentals”
Kussul N.M, Kovgar V.B., Koval M.Yu.
Kiev, 1997. – 67 p. (in Ukrainian)