Year 2021, Volume 9 , Issue 1, Pages 78 - 82 2021-01-30

Classification of the Agricultural Crops Using Landsat-8 NDVI Parameters by Support Vector Machine

Emrullah ACAR [1] , Müslime ALTUN [2]


Along with the data obtained from the developing remote sensing technologies, the use of machine learning techniques is widely employed in classification at a more effective and precise level. In this study, support vector machines (SVM) technique, one of the machine learning approaches, was utilized with the help of data obtained from satellite image, and it was aimed to classify agricultural products. Moreover, lentil and wheat products were employed for object detection, and Landsat-8 satellite was preferred as satellite imagery. In order to determine the plant indexes in the image, Landsat-8 image of the development period of agricultural products dated May 6, 2018 was used and 98 sample points were taken with the help of GPS on the pilot area. After that, the position of these points were transferred to Landsat-8 satellite image employing the QGIS program and NDVI values were calculated from these points, which corresponds to Landsat-8 NDVI image pixels. The obtained NDVI values were then utilized in the SVM as inputs. As a result, the accuracy of the overall system for crop classification on the pilot area was computed as 83.3%.
Remote Sensing, SVM, Landsat-8, NDVI, Crop Classification
  • [1] Q. Weng, "Introduction to Remote Sensing Systems, Data, Applications."Remote Perception of Natural Resources July 2013, pp 3-20
  • [2] T. Kavzoğlu, and İ. Çölkesen, "Remote Sensing Technologies and Applications." Sustainable Land Management Workshop In Turkey, 26-27 May 2011.
  • [3] Huang, J., Blanz, V., & Heisele, B. (2002, August). Face recognition using component-based SVM classification and morphable models. In International Workshop on Support Vector Machines (pp. 334-341). Springer, Berlin, Heidelberg.
  • [4] Kobayashi, N., Tani, H., Wang, X., & Sonobe, R. (2020). Crop classification using spectral indices derived from Sentinel-2A imagery. Journal of Information and Telecommunication, 4(1), 67-90.
  • [5] Htitiou, A., Boudhar, A., Lebrini, Y., Hadria, R., Lionboui, H., & Benabdelouahab, T. (2020). A comparative analysis of different phenological information retrieved from Sentinel-2 time series images to improve crop classification: A machine learning approach. Geocarto International, (just-accepted), 1-20.
  • [6] Acar, E., & ÖZERDEM, M. S. (2020). On a yearly basis prediction of soil water content utilizing sar data: a machine learning and feature selection approach. Turkish Journal of Electrical Engineering & Computer Sciences, 28(4), 2316-2330.
  • [7] Chakhar, A., Ortega-Terol, D., Hernández-López, D., Ballesteros, R., Ortega, J. F., & Moreno, M. A. (2020). Assessing the Accuracy of Multiple Classification Algorithms for Crop Classification Using Landsat-8 and Sentinel-2 Data. Remote Sensing, 12(11), 1735.
  • [8] Nasa U.S. Geological Survey. Landsat Data Continuity Mission ,February 2013, pp.1-17.
  • [9] A. Gönenç, " Comparison of NDVI and RVI Vegetation Indices Using Satellite Images. " 2019 Master Thesis, D.Ü, Institute of Science, Diyarbakır, 22-56
  • [10] N. Kobayashi, T.Tani, X. Wang, R. Sonobe, "Product classification using spectral indices derived from Sentinel-2A images, " 2019.
  • [11] Gonenc, A., OZERDEM, M. S., & Emrullah, A. C. A. R. (2019, July). Comparison of NDVI and RVI Vegetation Indices Using Satellite Images. In 2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics) (pp. 1-4). IEEE.
  • [12] P. Kumar, D.K. Gupta, V.N. Mishra, R. Prasad, "Comparison of spectral angle matching algorithms for crop classification using support vector machine, artificial neural network, and LISS IV data." International Journal of Remote Sensing 2015.
Primary Language en
Subjects Computer Science, Artifical Intelligence
Published Date January 2021
Journal Section Araştırma Articlessi
Authors

Orcid: 0000-0002-1897-9830
Author: Emrullah ACAR (Primary Author)
Institution: BATMAN UNIVERSITY
Country: Turkey


Orcid: 0000-0001-9787-3286
Author: Müslime ALTUN
Institution: BATMAN UNIVERSITY
Country: Turkey


Dates

Publication Date : January 30, 2021

Bibtex @research article { bajece863147, journal = {Balkan Journal of Electrical and Computer Engineering}, issn = {2147-284X}, address = {}, publisher = {Balkan Yayın}, year = {2021}, volume = {9}, pages = {78 - 82}, doi = {10.17694/bajece.863147}, title = {Classification of the Agricultural Crops Using Landsat-8 NDVI Parameters by Support Vector Machine}, key = {cite}, author = {Altun, Müslime} }
APA Acar, E , Altun, M . (2021). Classification of the Agricultural Crops Using Landsat-8 NDVI Parameters by Support Vector Machine . Balkan Journal of Electrical and Computer Engineering , 9 (1) , 78-82 . DOI: 10.17694/bajece.863147
MLA Acar, E , Altun, M . "Classification of the Agricultural Crops Using Landsat-8 NDVI Parameters by Support Vector Machine" . Balkan Journal of Electrical and Computer Engineering 9 (2021 ): 78-82 <https://dergipark.org.tr/en/pub/bajece/issue/60125/863147>
Chicago Acar, E , Altun, M . "Classification of the Agricultural Crops Using Landsat-8 NDVI Parameters by Support Vector Machine". Balkan Journal of Electrical and Computer Engineering 9 (2021 ): 78-82
RIS TY - JOUR T1 - Classification of the Agricultural Crops Using Landsat-8 NDVI Parameters by Support Vector Machine AU - Emrullah Acar , Müslime Altun Y1 - 2021 PY - 2021 N1 - doi: 10.17694/bajece.863147 DO - 10.17694/bajece.863147 T2 - Balkan Journal of Electrical and Computer Engineering JF - Journal JO - JOR SP - 78 EP - 82 VL - 9 IS - 1 SN - 2147-284X- M3 - doi: 10.17694/bajece.863147 UR - https://doi.org/10.17694/bajece.863147 Y2 - 2021 ER -
EndNote %0 Balkan Journal of Electrical and Computer Engineering Classification of the Agricultural Crops Using Landsat-8 NDVI Parameters by Support Vector Machine %A Emrullah Acar , Müslime Altun %T Classification of the Agricultural Crops Using Landsat-8 NDVI Parameters by Support Vector Machine %D 2021 %J Balkan Journal of Electrical and Computer Engineering %P 2147-284X- %V 9 %N 1 %R doi: 10.17694/bajece.863147 %U 10.17694/bajece.863147
ISNAD Acar, Emrullah , Altun, Müslime . "Classification of the Agricultural Crops Using Landsat-8 NDVI Parameters by Support Vector Machine". Balkan Journal of Electrical and Computer Engineering 9 / 1 (January 2021): 78-82 . https://doi.org/10.17694/bajece.863147
AMA Acar E , Altun M . Classification of the Agricultural Crops Using Landsat-8 NDVI Parameters by Support Vector Machine. Balkan Journal of Electrical and Computer Engineering. 2021; 9(1): 78-82.
Vancouver Acar E , Altun M . Classification of the Agricultural Crops Using Landsat-8 NDVI Parameters by Support Vector Machine. Balkan Journal of Electrical and Computer Engineering. 2021; 9(1): 78-82.
IEEE E. Acar and M. Altun , "Classification of the Agricultural Crops Using Landsat-8 NDVI Parameters by Support Vector Machine", Balkan Journal of Electrical and Computer Engineering, vol. 9, no. 1, pp. 78-82, Jan. 2021, doi:10.17694/bajece.863147