The problem of classification and justification of the methodology for selecting classes of  objects subject to recognition in a multispectral space image

Rovshan Ramazanov

doi:10.56143/engineer-tstu.v3i2.74

Rovshan Ramazanov National Aerospace Agency / Research Institute of Aerospace Informatics

DOI: https://doi.org/10.56143/engineer-tstu.v3i2.74

Аннотация

In the article, the specific task of Remote Sensing is being investigated to classify recognizable classes from objects consisting of 12 different types of vegetation soil features. The stages of solving this problem were selected and justified and the territorial areas where the test areas were formed for the development and testing of the classifier were determined. Based on information obtained from satellite and IKONOS, tables are provided that provide quantitative assessments for various class combinations of vegetation objects in the images formed from the satellite image of the studied area. The tables reflect statistical characteristics allow for preliminary assessments of the representativeness of the clusters. The article presents combinations of histograms for various variants of combinations of classified vegetation classes and provides tables of unsuitability using decision rules based on statistical characteristics.

Литература

[1] Sulaymanov S., Kamilov X. Developing a method [1] Acton, S. T.: On unsupervised segmentation of remotely sensed imagery using nonlinear regression. International Journal of Remote Sensing. 17, 1407–1415 (1996)
[2] Blaschke, T.: Object based image analysis for remote sensing. ISPRS International Journal of Photogrammetry and Remote Sensing . 65 , 2-16 (2010). doi:10.1016/j.isprsjprs.2009.06.004
[3] Burrough, P. A., Mcdonnel, R. A.: Principle of Geographical Information Systems, Oxford University Press .(1998). doi:10.1080/01431160210144499
[4] Bruin, D.S., Molenaar, M.: Remote sensing and geographical information systems. In Spatial Statistics for Remote Sensing. Stein, A., Meer, F.V., Gorte, B.G.H.:(eds) Dordrecht Kluwer Academic . 41–54 (1999)
[5] Frohn, R. C., Autrey, B. C., Lane, C. R., Reif, M.: Segmentation and object-oriented classification of wetlands in a karst Florida landscape using multi-season Landsat-7 ETM+ imagery. International Journal of Remote Sensing . 32, 1471-1489 (2011). doi:10.1080/01431160903559762
[6] Geneletti, D., Gorte, B. G. H.: A method for object-oriented land cover classification combining Landsat TM data and aerial photographs. International Journal of Remote Sensing. 24, 1273-1286 (2003). https://doi.org/10.1080/01431160210144499
[7] Navulur, K.: Multispectral Image Analysis Using the Object-Oriented Paradigm. CRC Press, Taylor & Francis Group. (2007)
[8] Stein, A., Meer, F.V., Gorte, B.G.H. : (eds) Spatial Statistics for Remote Sensing. Dordrecht: Kluwer Academic. (1999)
[9] GEOINFORMATION TECHNOLOGIES IN REMOTE SENSING TACKS BASED ON HIGH RESOTION SATTELITE IMAGES E.RAMAZANOVA, R.RAHIMOV, D.QAHRAMANOVA, F.SAMEDOV INTERNATIONAL CONFERENCE DEDICATED TO THE 90-TH ANNIVERSARY OF ACADEMICIAN AZAD MIRZAJANZADE , BAKU, AZERBAIJAN ,13-14 DESEMBER ( 2018)
[10] Rauf Ragimov, Elchin Isgenderzade, Rovshan Ramazanov, Farid Samadov, Shana Jahidzada ”An innovative technology for aerospace monitoring of geotechnical systems based on the use of unmanned aerial vehicles (UAVs). Azerbaijan National Aerospace Agency 74th International Astronautical Congress Baku, Azerbaijan 2-6 October (2023).