Dr. Georgi Jelev Profile

Dr. Georgi Jelev

at Space Research and Technology Institute

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Publications (4)

Proceedings Article | 28 October 2022 Paper

Evaluation of PhenoCam phenology of barley

Dessislava Ganeva, Milen Chanev, Lachezar Filchev, Georgi Jelev, Darina Valcheva

Proceedings Volume 12262, 1226208 (2022) https://doi.org/10.1117/12.2636335

KEYWORDS: Sensors, Earth observing sensors, Satellites, RGB color model, Near infrared, Vegetation, Statistical modeling, Spatial resolution, Remote sensing

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Proceedings Article | 27 June 2019 Paper

Applicability of parametric and nonparametric regression models for retrieval of crop canopy parameters for winter rapeseed and wheat crops using Sentinel-2 multispectral data

Dessislava Ganeva, Eugenia Roumenina, Georgi Jelev, Marin Banov, Veneta Krasteva, Victor Kolchakov

Proceedings Volume 11174, 111740J (2019) https://doi.org/10.1117/12.2533651

KEYWORDS: Data modeling, Calibration, Vegetation, Remote sensing, Short wave infrared radiation, Reflectivity, Nitrogen

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Proceedings Article | 23 October 2014 Paper

Algorithms for lineaments detection in processing of multispectral images

D. Borisova, G. Jelev, V. Atanassov, Petia Koprinkova-Hristova, K. Alexiev

Proceedings Volume 9245, 92451L (2014) https://doi.org/10.1117/12.2067245

KEYWORDS: Multispectral imaging, Satellites, Image filtering, Detection and tracking algorithms, Image processing, Satellite imaging, Earth observing sensors, Visualization, Geographic information systems, Evolutionary algorithms

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Satellite remote sensing is a universal tool to investigate the different areas of Earth and environmental sciences. The advancement of the implementation capabilities of the optoelectronic devices which are long-term-tested in the laboratory and the field and are mounted on-board of the remote sensing platforms further improves the capability of instruments to acquire information about the Earth and its resources in global, regional and local scales. With the start of new high-spatial and spectral resolution satellite and aircraft imagery new applications for large-scale mapping and monitoring becomes possible. The integration with Geographic Information Systems (GIS) allows a synergistic processing of the multi-source spatial and spectral data. Here we present the results of a joint project DFNI I01/8 funded by the Bulgarian Science Fund focused on the algorithms of the preprocessing and the processing spectral data by using the methods of the corrections and of the visual and automatic interpretation. The objects of this study are lineaments. The lineaments are basically the line features on the earth's surface which are a sign of the geological structures. The geological lineaments usually appear on the multispectral images like lines or edges or linear shapes which is the result of the color variations of the surface structures. The basic geometry of a line is orientation, length and curve. The detection of the geological lineaments is an important operation in the exploration for mineral deposits, in the investigation of active fault patterns, in the prospecting of water resources, in the protecting people, etc. In this study the integrated approach for the detecting of the lineaments is applied. It combines together the methods of the visual interpretation of various geological and geographical indications in the multispectral satellite images, the application of the spatial analysis in GIS and the automatic processing of the multispectral images by Canny algorithm, Directional Filter and Neural Network. Landsat multispectral images of the Eastern Rhodopes in Bulgaria for carrying out the procedure are used. Canny algorithm for extracting edges represents series of filters (Gaussian, Sobel, etc.) applied to all bands of the image using the free IDL source. Directional Filter is applied to sharpen the image in a specific preferred direction. Another method is the Neural Network algorithm for recognizing lineaments. The lineaments are effectively extracted using different methods of automatic. The results from the above mentioned methods are compared to the results derived from the visual interpretation of satellite images and from the geological map. In conclusion, the rose diagrams of the distribution of the geological lineaments and the maps of their density are completed.

Proceedings Article | 17 October 2013 Paper

Recurrent neural networks for automatic clustering of multispectral satellite images

Petia Koprinkova-Hristova, Kiril Alexiev, Denitsa Borisova, Georgi Jelev, Valentin Atanassov

Proceedings Volume 8892, 88920X (2013) https://doi.org/10.1117/12.2029191

KEYWORDS: Neurons, Satellites, Landsat, Satellite imaging, Neural networks, Earth observing sensors, Multispectral imaging, Image classification, Orthophoto maps, Artificial neural networks

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