IECS remote sensing group


  • Juris Siņica-Siņavskis, Gunta Grūbe. Forest Stand Volume Estimation by Species from Sentinel-2 and LiDAR Data Using Regression Models. 2022 18th Biennial Baltic Electronics Conference (BEC), 1-5 pp. IEEE, 2022, doi: 10.1109/BEC56180.2022.9935590.
  • Linda Gulbe, Juris Zarins, Ints Mednieks, “Automated delineation of microstands in hemiboreal mixed forests using stereo GeoEye-1 data”. MPDI Open Access Journals, Remote Sens. 18 March 2022.
  • Grigorijs Goldbergs “Impact of Base-to-Height Ratio on Canopy Height Estimation Accuracy of Hemiboreal Forest Tree Species by Using Satellite and Airborne Stereo Imagery “ MPDI Open Access Journals.  27 July 2021. , Remote Sens. 2021, 13(15), 2941.
  • J.Sinica-Sinavskis, R.Dinuls, J.Zarins and I.Mednieks “Automatic tree species classification from Sentinel-2 images using deficient inventory data”, 17th Biennial Conference on Electronics and Embedded Systems. Tallinn, Estonia, October 6-8, 2020, doi: 10.1109/BEC49624.2020.9276810.
  • R.Dinuls, I.Mednieks “Nonparametric Classification of Satellite Images.” Proceedings of the 2018 International Conference on Mathematics and Statistics. ACM, New York, NY, USA, 2018, pp. 64-68. DOI:10.1145/3274250.3274260.
  • A. Lorencs, I. Mednieks & J. Sinica-Sinavskis. Selection of informative hyperspectral band subsets based on entropy and correlation. International Journal of Remote Sensing, 2018, DOI: 10.1080/01431161.2018.1468107
  • A.Lorencs, I.Mednieks, J.Sinica-Sinavskis “Selection of Informative Bands for Classification of Hyperspectral Images Based on Entropy”, Proc. of BEC2016, the 15th Biennial Conference on Electronics and Embedded Systems. Tallinn, Estonia on October 3-5, 2016, pp. 135-138, DOI: 10.1109/BEC.2016.7743747.
  • A.Lorencs, J. Sinica-Sinavskis, D.Jakovels, I.Mednieks. Melanoma-Nevus Discrimination Based on Image Statistics in Few Spectral Channels. Elektronika ir Elektrotechnika. Kaunas: Technologija, Vol.22, No.2, pp. 66-72, 2016. DOI: 10.5755/j01.eie.22.2.12173.
  • A.Lorencs, I.Mednieks, J. Sinica-Sinavskis. Classification of Multisensor Images with Different Spatial Resolution. Elektronika ir Elektrotechnika. Kaunas: Technologija, Vol.21, No.5, pp. 81–85, 2015. DOI: 10.5755/j01.eee.21.5.13333.
  • D.Jakovels, I.Saknite, D.Bliznuks, J.Spīgulis, R.Kadiķis. Benign – A typical nevi discrimination using diffuse reflectance and fluorescence multispectral imaging system. BioPhotonics, 2015 International Conference on, IEEE, pp. 1-4, May 2015.
  • R.Kadiķis. Registration method for multispectral skin images. Proceedings of 25th International Conference Radioelektronika 2015, IEEE, pp. 232-235, April 2015.
  • A.Lorencs, I.Mednieks, J. Sinica-Sinavskis. Simplified Classification of Multispectral Image Fragments. Elektronika ir Elektrotechnika. Kaunas: Technologija, Vol.20, No.6, pp. 136–139, 2014. DOI: 10.5755/j01.eee.20.6.7286.
  • Lorencs, Yu. Sinitsa-Sinyavskis. “Analysis of a two-stage Bayes classifiers construction method: The 2-dimensional case,” Automatic Control and Computer Sciences, Volume 47, Issue 5, pp. 254-266, 2013. DOI: 10.3103/S0146411613050040.
  • 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. DOI: 10.1109/JSTARS.2012.2196978.
  • R. Dinuls, A. Lorencs, I. Mednieks. “Using Consolidated Covariance Image for Discrimination of Habitats,” Proceedings of the 13th Biennial Baltic Electronics Conference, Tallinn, Estonia, pp.299-302, 2012. DOI: 10.1109/BEC.2012.6376876.
  • I.Mednieks. “A Method for Correction of Rural Multispectral Aerial Image Mosaics.” Proceedings of the 13th Biennial Baltic Electronics Conference, Tallinn,  pp.295-298, 2012. DOI: 10.1109/BEC.2012.6376875.
  • 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. DOI: 10.3103/S0146411612050045.
  • 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. DOI: 10.3103/S0146411611020039.
  • R. Dinuls, A. Lorencs, I. Mednieks. “Performance Comparison of Methods for Tree Species Classification in Multispectral Images,” Elektronika ir Elektrotechnika. Kaunas: Technologija, 2011, No.5(111), pp. 119–122. Link.
  • G.Erins, A.Lorencs, I.Mednieks, J.Sinica-Sinavskis. “Tree Species Classification in Mixed Baltic Forest,” Proceedings of 3rd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2011, pp.1-4. DOI: 10.1109/WHISPERS.2011.6080857.
  • A.A.Lorencs. Multidimensional Observation Plans Inducing Nondegeneracy of Information Matrixes of Regression Models. Automatic Control and Computer Sciences, 2010, Vol.44, No.2, pp.69-77.
  • A.Lorencs, J. Sinica-Sinavskis. One method of image processing and its numerical analysis. Elektronika ir Elektrotechnika. Kaunas; Technologija, 2010. No.7 (103), pp.25-29.
  • I.Mednieks. Morphology-Based Approach to Detection of Free Form Line Objects in Grayscale Images. Elektronika ir Elektrotechnika. Kaunas: Technologija, 2010, No. 8(104), pp. 27-30.
  • 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.
  • I.Mednieks, A.Skageris. “Real Time Image Processing for Object Detection”, Elektronika ir Elektrotechnika. Kaunas: Technologija, 2009, No.4(92), p.33-36.
  • A.Lorencs, I.Mednieks, J.Sinica-Sinavskis. Biomedical Image Processing Based on Regression Models. NBC – 14th Nordic-Baltic Conference on Biomedical Engineering and Medical Physics, Riga, June 16-20, 2008. IFMBE Proceedings, Vol.20, pp. 536-539.
  •  I.Mednieks. Object detection in grayscale images based on covariance features. Proceedings of ICSES 2008, Krakow, September 2008, pp. 205-209.