Journals A-Z
All Subjects
Free Journals
sciepub.com
Applied Ecology and Environmental Sciences
ISSN (Print): 2328-3912 ISSN (Online): 2328-3920 Website: http://www.sciepub.com/journal/aees Editor-in-chief: Alejandro González Medina
Open Access
Journal Browser
Go
Issue 6, Volume 8
Article Metrics
  • 1585
    Views
  • 1816
    Saves
  • 0
    Citations
  • 1
    Likes
Export Article
Applied Ecology and Environmental Sciences. 2020, 8(6), 459-464
DOI: 10.12691/aees-8-6-18
Open AccessArticle

An Assessment of Artificial Neural Networks, Support Vector Machines and Decision Trees for Land Cover Classification Using Sentinel-2A Data

Rahel Hamad1, 2,

1GIS and Remote Sensing Department, Scientific Research Centre (SRC), Delzyan Campus, Soran University, Soran 44008, Erbil, Iraq

2Department of Petroleum Geosciences, Faculty of Science, Delzyan Campus, Soran University, Soran 44008, Erbil, Iraq

Pub. Date: September 29, 2020
View Full Text Full Text PDF Full Text ePUB

Cite this paper:
Rahel Hamad. An Assessment of Artificial Neural Networks, Support Vector Machines and Decision Trees for Land Cover Classification Using Sentinel-2A Data. Applied Ecology and Environmental Sciences. 2020; 8(6):459-464. doi: 10.12691/aees-8-6-18

Abstract

Remotely sensed images serve as a valuable source of present and archival information since they provide the geographical distribution of natural and cultural features both spatially and temporally, as well as objects on the earth’s surface. Three machine learning classifiers, namely artificial neural networks (ANN), support vector machines (SVM), and decision tree (DT) algorithms, were applied in order to classify the Sentinel-2A data over the city of Soran. The differences in classification accuracies were evaluated by the confusion matrix. The supervised ANNs obtained the most accurate classification accuracy as compared with support SVM and DTs algorithms. Furthermore, the overall accuracy assessment of ANN was 90%, with SVM at 65%, while DTs were 60%. It can be concluded that ANNs can provide the best classification machine learning technique for land cover classification.

Keywords:
Soran remote sensing GIS overfitting hyperplane

Creative CommonsThis work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

References:

[1]  Huang, C., L. Davis, and J. Townshend, An assessment of support vector machines for land cover classification. International Journal of remote sensing, 2002. 23(4): p. 725-749.
 
[2]  Li, J. and D.P. Roy, A global analysis of Sentinel-2A, Sentinel-2B and Landsat-8 data revisit intervals and implications for terrestrial monitoring. Remote Sensing, 2017. 9(9): p. 902.
 
[3]  Lu, D., S. Hetrick, and E. Moran, Impervious surface mapping with Quickbird imagery. International journal of remote sensing, 2011. 32(9): p. 2519-2533.
 
[4]  Lu, D. and Q. Weng, A survey of image classification methods and techniques for improving classification performance. International journal of Remote sensing, 2007. 28(5): p. 823-870.
 
[5]  Anderson, J.R., A land use and land cover classification system for use with remote sensor data. Vol. 964. 1976: US Government Printing Office.
 
[6]  Thai, L., T. Hai, and N.T. Thuy, Image classification using support vector machine and artificial neural network. International Journal of Information Technology and Computer Science (IJITCS), 2012. 4(5): p. 32-38.
 
[7]  Foody, G., Supervised image classification by MLP and RBF neural networks with and without an exhaustively defined set of classes. International Journal of Remote Sensing, 2004. 25(15): p. 3091-3104.
 
[8]  Verbeke, L., F. Vancoillie, and R. De Wulf, Reusing back-propagation artificial neural networks for land cover classification in tropical savannahs. International Journal of Remote Sensing, 2004. 25(14): p. 2747-2771.
 
[9]  Foody, G.M. and A. Mathur, Toward intelligent training of supervised image classifications: directing training data acquisition for SVM classification. Remote Sensing of Environment, 2004. 93(1-2): p. 107-117.
 
[10]  Mitra, P., B.U. Shankar, and S.K. Pal, Segmentation of multispectral remote sensing images using active support vector machines. Pattern recognition letters, 2004. 25(9): p. 1067-1074.
 
[11]  Lawrence, R., et al., Classification of remotely sensed imagery using stochastic gradient boosting as a refinement of classification tree analysis. Remote sensing of environment, 2004. 90(3): p. 331-336.
 
[12]  DeFries, R. and J.C.-W. Chan, Multiple criteria for evaluating machine learning algorithms for land cover classification from satellite data. Remote Sensing of Environment, 2000. 74(3): p. 503-515.
 
[13]  Sohn, Y., E. Morán, and F. Gurri, Deforestation in North-Central Yucatan (1985-1995)- Mapping secondary succession of forest and agricultural land use in Sotuta using the cosine of the angle concept. Photogrammetric engineering and remote sensing, 1999. 65: p. 947-958.
 
[14]  Sohn, Y. and N.S. Rebello, Supervised and unsupervised spectral angle classifiers. Photogrammetric engineering and remote sensing, 2002. 68(12): p. 1271-1282.
 
[15]  Dymova, L., P. Sevastianov, and P. Bartosiewicz, A new approach to the rule-base evidential reasoning: Stock trading expert system application. Expert Systems with Applications, 2010. 37(8): p. 5564-5576.
 
[16]  Yang, J.-B., et al., Belief rule-base inference methodology using the evidential reasoning approach-RIMER. IEEE Transactions on systems, Man, and Cybernetics-part A: Systems and Humans, 2006. 36(2): p. 266-285.
 
[17]  Franklin, S. and M. Wulder, Remote sensing methods in medium spatial resolution satellite data land cover classification of large areas. Progress in Physical Geography, 2002. 26(2): p. 173-205.
 
[18]  Congalton, R.G. and L. Plourde, Quality assurance and accuracy assessment of information derived from remotely sensed data. Manual of geospatial science and technology, 2002: p. 349-361.
 
[19]  Foody, G.M., Status of land cover classification accuracy assessment. Remote sensing of environment, 2002. 80(1): p. 185-201.
 
[20]  Szantoi, Z., et al., Classifying spatially heterogeneous wetland communities using machine learning algorithms and spectral and textural features. Environmental monitoring and assessment, 2015. 187(5): p. 262.
 
[21]  Chen, J., A comparison of four data mining models: bayes, neural network. SVM and decision trees in identifying syndromes in coronary heart disease, 2007. 4491: p. 2007.
 
[22]  Khatami, R., G. Mountrakis, and S.V. Stehman, A meta-analysis of remote sensing research on supervised pixel-based land-cover image classification processes: General guidelines for practitioners and future research. Remote Sensing of Environment, 2016. 177: p. 89-100.
 
[23]  Otukei, J.R. and T. Blaschke, Land cover change assessment using decision trees, support vector machines and maximum likelihood classification algorithms. International Journal of Applied Earth Observation and Geoinformation, 2010. 12: p. S27-S31.
 
[24]  Mohamed, A.E., Comparative study of four supervised machine learning techniques for classification. International Journal of Applied, 2017. 7(2).
 
[25]  Song, Y.-Y. and L. Ying, Decision tree methods: applications for classification and prediction. Shanghai archives of psychiatry, 2015. 27(2): p. 130.
 
[26]  Friedl, M.A. and C.E. Brodley, Decision tree classification of land cover from remotely sensed data. Remote sensing of environment, 1997. 61(3): p. 399-409.
 
[27]  Hamad, R., A remote sensing and GIS-based analysis of urban sprawl in Soran District, Iraqi Kurdistan. SN Applied Sciences, 2020. 2(1): p. 24.
 
[28]  Atkinson, P.M. and A.R. Tatnall, Introduction neural networks in remote sensing. International Journal of remote sensing, 1997. 18(4): p. 699-709.
 
[29]  Liu, C., et al., Comparison of neural networks and statistical methods in classification of ecological habitats using FIA data. Forest Science, 2003. 49(4): p. 619-631.
 
[30]  Kesikoglu, M.H., et al., Performance of ANN, SVM and MLH techniques for land use/cover change detection at Sultan Marshes wetland, Turkey. Water Science and Technology, 2019. 80(3): p. 466-477.
 
[31]  Bhatta, B., Remote sensing and GIS. 2008: Oxford University Press, USA.
 
[32]  Civco, D.L., Artificial neural networks for land-cover classification and mapping. International journal of geographical information science, 1993. 7(2): p. 173-186.
 
[33]  Kanellopoulos, I. and G.G. Wilkinson, Strategies and best practice for neural network image classification. International Journal of Remote Sensing, 1997. 18(4): p. 711-725.
 
[34]  Todorov, T.I., R.R. Stewart, and D.P. Hampson, Porosity prediction using attributes from 3C–3D seismic data. 1998, CREWES research report, 10, Chapt-52.
 
[35]  Lillesand, T., R.W. Kiefer, and J. Chipman, Remote sensing and image interpretation. 2014: John Wiley & Sons.
 
[36]  Wang, Q., Y. Tian, and D. Liu, Adaptive FH-SVM for Imbalanced Classification. IEEE Access, 2019. 7: p. 130410-130422.
 
[37]  Rudrapal, D. and M. Subhedar, Land cover classification using support vector machine. International Journal of Engineering Research & Technology (IJERT), 2015. 4(09).
 
[38]  Kranjčić, N., et al., Support Vector Machine Accuracy Assessment for Extracting Green Urban Areas in Towns. Remote Sensing, 2019. 11(6): p. 655.
 
[39]  Awad, M. and R. Khanna, Support vector machines for classification, in Efficient Learning Machines. 2015, Springer. p. 39-66.
 
[40]  Shi, D. and X. Yang, Support vector machines for land cover mapping from remote sensor imagery, in Monitoring and Modeling of Global Changes: A Geomatics Perspective. 2015, Springer. p. 265-279.
 
[41]  Guo, G., S.Z. Li, and K.L. Chan, Support vector machines for face recognition. Image and Vision computing, 2001. 19(9-10): p. 631-638.
 
[42]  Xie, Z., et al., Classification of land cover, forest, and tree species classes with ZiYuan-3 multispectral and stereo data. Remote Sensing, 2019. 11(2): p. 164.
 
[43]  Osei-Bryson, K.-M., Post-pruning in decision tree induction using multiple performance measures. Computers & operations research, 2007. 34(11): p. 3331-3345.
 
[44]  Sahoo, S. and P. Raj, EVALUATION OF DIFFERENT TECHNIQUES TO DETECT LAND USE/LAND COVER OVER AN AREA. Journal of Spatial Hydrology, 2019. 14(2).
 
[45]  Sharma, R., A. Ghosh, and P. Joshi, Decision tree approach for classification of remotely sensed satellite data using open source support. Journal of Earth System Science, 2013. 122(5): p. 1237-1247.
 
[46]  Michalski, R.S., J.G. Carbonell, and T.M. Mitchell, Machine Learning: An Artificial Intelligence Approach, vol. 1. Tioga, Palo Alto, California, 1983.
 
[47]  Pantazi, X.-E., D. Moshou, and D. Bochtis, Intelligent Data Mining and Fusion Systems in Agriculture. 2019: Academic Press.
 
[48]  Coleman, J. and J.S. Coleman, Introducing speech and language processing. 2005: Cambridge university press.
 
[49]  Korkobi, T., M. Djemel, and M. Chtourou, Stability analysis of neural networks-based system identification. Modelling and Simulation in Engineering, 2008. 2008.
 
[50]  He, H. and Y. Ma, Imbalanced learning: foundations, algorithms, and applications. 2013: John Wiley & Sons.
 
[51]  Veropoulos, Campell C., and Cristanini N., “Controlling the sensitivity of support vector machines,” in Proceeding of the International Joint Conference on Artificial Intelligence (Stockholm, Sweden). 1999: p. pp. 55-60.
 
[52]  Kowsari, K., et al. Hdltex: Hierarchical deep learning for text classification. in 2017 16th IEEE international conference on machine learning and applications (ICMLA). 2017. IEEE.
 
[53]  Punia, M., P.K. Joshi, and M. Porwal, Decision tree classification of land use land cover for Delhi, India using IRS-P6 AWiFS data. Expert systems with Applications, 2011. 38(5): p. 5577-5583.
 
[54]  Kumar, G.D. and G.D. Kumar, Machine Learning Techniques for Improved Business Analytics. 2018: IGI Global.
 
[55]  Choy, M. and P. Flom. Building decision trees from decision stumps. in SAS Global Forum. 2010.
 
[56]  Ramezankhani, A., et al., Decision tree-based modelling for identification of potential interactions between type 2 diabetes risk factors: a decade follow-up in a Middle East prospective cohort study. BMJ open, 2016. 6(12): p. e013336.
 
Manuscript template