J.Sinica-Sinavskis is in computer science, a field at the remote sensing application between hyperspectral imaging, multispectral image processing, synthetic-aperture radar image processing, statistical modelling, and object based image analysis. J.Sinica- Sinavskis is working in the EDI in the area of image processing since 2006. J.Sinica- Sinavskis received the master degree in Mathematics from University of Latvia in 2009, and since 2019 he holds a Ph.D. in Computer Science at University of Latvia. He is a member of Latvian Association of Statisticians (LSA), The Remote Sensing and Photogrammetry Society (RSPSoc), and IEEE Geoscience and Remote Sensing Society (GRSS). He works closely with application experts, in order to focus the algorithm to solve the problem effectively.
Participation in projects
- Object based context aware self-learning network for land cover classification (Dynland-2)
- Satellite remote sensing- based forest stock estimation technology (WoodStock) #ESIF
- Dynamic land use monitoring (NevKlas) #ESIF
- Dynamic land use monitoring by fusion of satellite data (DynLand) #ESA
- Innovative technologies for acquisition and processing of biomedical images (InBiT) #ESIF
- Multi-model development technology for .NET application projects (MEDUS) #ESIF
- Cyber-physical systems, ontologies and biophotonics for safe&smart city and society (GUDPILS) #SRP (VPP)
The most important publications
- Sinica-Sinavskis J., Dinuls R., Zarins J., Mednieks I., Automatic tree species classification from Sentinel 2 images using deficient inventory data, 2020 17th Biennial Baltic Electronics Conference (BEC), Tallinn, Estonia, pp. 1-6, 2020.
- A. Lorencs, I. Mednieks & J. Sinica-Sinavskis. Selection of informative hyperspectral band subsets based on entropy and correlation. International Journal of Remote Sensing, 39:20, pp. 6931-6948, 2018. DOI: 10.1080/01431161.2018.1468107
- LORENCS, A., MEDNIEKS, I., SINICA-SINAVSKIS, J., PUKITIS M. Fusion of Multisensor Data Based on Different Multidimensional Distributions, Electronics and Electrical Engineering (Elektronika ir Elektrotechnika, Vol. 22, No.4, pp. 68-72, 2016.
- LORENCS, A., Sinica-Sinavskis, j., Jakovels, d., Mednieks, i., 2016. Melanoma-Nevus Discrimination Based on Image Statistics in Few Spectral Channels, Electronics and Electrical Engineering (Elektronika ir Elektrotechnika, Vol. 22, No2, pp. 66-72
- LORENCS, A., MEDNIEKS, I., SINICA-SINAVSKIS, J., 2015. Classification of Multisensor Images with Different Spatial Resolution, Electronics and Electrical Engineering, Vol. 21, No. 5, pp.81-85
- LORENCS, A., MEDNIEKS, I., SINICA-SINAVSKIS, J. 2014. Simplified Classification of Multispectral Image Fragments. Electronics and Electrical Engineering. Kaunas: Technologija, 20(6), pp. 136–139.
- A.Lorencs, J.Sinica-Sinavskis, “Analysis of two-stage Bayes classifiers construction method: 2-dimensional case,” Automatic Control and Computer Sciences, September 2013, Vol. 47, No. 5, pp. 254-266, 2013.
- A. Lorencs, Yu. Sinitsa-Sinyavskis. "A two-stage method for building classifiers," Automatic Control and Computer Sciences, September 2012, Volume 46, Issue 5, pp. 214-222.
- R. Dinuls, G. Erins, A. Lorencs, I. Mednieks, and J. Sinica-Sinavskis, “Tree species identification in mixed Baltic forest using LiDAR and multispectral data,” IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 5, no. 2, pp. 594–603, 2012.
- A. Lorencs, I. Mednieks, J. Sinica-Sinavskis, “Design problems of tree species classifiers for multispectral images,” Automatic Control and Computer Sciences, Vol. 45, No. 2, pp. 61-69, 2011.
- A.Lorencs, I.Mednieks, J.Sinica-Sinavskis. “Fast Object Detection in Digital Grayscale Images”, Proceedings of the Latvian Academy of Sciences. Section B., 2009, Vol.63, No.3, pp.116-124.