Comparison of Different Classification Algorithms for The Detection of Changes on Water Bodies; Karakaya Dam Lake

Yıl 2020, Cilt: 1 Sayı: 1, 27 - 34, 15.06.2020

Ahmet Tarık Torun , Halil İbrahim Gündüz

Öz

Optimum management of water and water bodies is crucial in ensuring and maintaining the natural ecosystem cycle. Benefits from wetlands in the world and in our country keep humanity alive. Resources that are of vital importance should be monitored and changes should be observed. Thanks to the science of remote sensing, researchers in many parts of the world can monitor changes in the waters of the earth through satellite imagery and terrestrial supporting studies. The main component of change detection in remote sensing is the classification process. Nowadays, the Classification process has reached different dimensions with the contributions of artificial intelligence and machine learning algorithms. The emergence of different classification techniques also affected the results obtained from the analyzes. In this study, the change occurred between 1990-2000-2010-2019 in Karakaya Dam Lake, which is included in the borders of Malatya - Elazig provinces, was observed. In this context, supervised classification processes and change detection analyzes were performed using Landsat satellite data with maximum likelihood, neural network, support vector machine and decision tree algorithms. For detecting the change analysis, the lake boundaries obtained from official sources were used and compared. The data obtained as a result of the study were compared for each technique and the amount of change was interpreted.

Anahtar Kelimeler

Classification, Change Detection

Kaynakça

• Calò, F., Notti, D., Galve, J. P., Abdikan, S., Görüm, T., Orhan, O., Makineci H.B., Pepe A., Yakar M. & Sanli, F.B. (2018). A multi-source data approach for the investigation of land subsidence in the Konya basin, Turkey.International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 42(3/W4).
• Çölkesen, İ. (2009). Uzaktan algılamada ileri sınıflandırma tekniklerinin karşılaştırılması ve analizi (Master Thesis) Gebze Technical University, Kocaeli, Turkey.
• Dean, A.M., Smith, G.M. (2003). An Evaluation of Perparcel Land Cover Mapping Using Maximum Likelihood Class Probabilities. International Journal of Remote Sensing, 24 (14), 2905-2920.
• Dixon, B., Candade, N. (2008). Multispectral landuse classification using neural networks and support vector machines: one or the other, or both?. International Journal of Remote Sensing, 29(4), 1185-1206.
• Foody, G. M., Mathur, A. (2004). Toward intelligent training of supervised image classifications: directing training data acquisition for SVM classification. Remote Sensing of Environment, 93(1-2), 107-117.
• Foody, G.M., & Arora, M. K. (1997). An evaluation of some factors affecting the accuracy of classification by an artificial neural network. International Journal of Remote Sensing, 18(4), 799-810.
• Foody, G.M., Campbell, N.A., Trodd, N.M., & Wood, T.F. (1992). Derivation and applications of probabilistic measures of class membership from the maximum-likelihood classification. Photogrammetric engineering and remote sensing, 58(9), 1335-1341.
• Foody, G.M., Mathur A. (2004). A relative evaluation of multiclass image classification by support vector machines. IEEE Transactions on Geoscience and Remote Sensing, 42, 1335–1343.
• Fuping, G., Runsheng, W., Yongjiang, W., & Zhengwen, F. (2011). The classification method based on remote sensing techniques for land use and cover. Remote Sensing for Land & Resources, 11(4), 40-45.
• Goung, L., & Zheng, T. (1993). Stereo matching using artificial neural networks. International Archives of Photogrammetry and Remote Sensing, 29, 417-417.
• Huang, C., Davis, L.S., & Townshend, J.R.G. (2002). An assessment of support vector machines for land cover classification, International Journal of Remote Sensing, 23(4), 725-749.
• Huang, C., Yang, L. (2001). Synergistic use of FIA data and landsat 7 ETM+ images for large area forest mapping. In The Thirty-fifth Annual Midwest Forest Mensurationists and the Third Annual FIA Symposium, Traverse City, MI, US.
• Ingram, J.C., Dawson, T.P., & Whittaker, R.J. (2005). Mapping tropical forest structure in southeastern Madagascar using remote sensing and artificial neural networks. Remote Sensing of Environment, 94(4), 491-507.
• Jensen, J.R., Qiu, F., & Ji, M. (1999). Predictive modelling of coniferous forest age using statistical and artificial neural network approaches applied to remote sensor data. International Journal of Remote Sensing, 20(14), 2805-2822.
• Kavzoglu, T., Colkesen, I. (2009). A kernel functions analysis for support vector machines for land cover classification. International Journal of Applied Earth Observation and Geoinformation, 11(5), pp.352-359.
• Kavzoğlu, T., Çölkesen, İ. (2010). Karar ağaçları ile uydu görüntülerinin sınıflandırılması: Kocaeli örneği. Harita Teknolojileri Elektronik Dergisi, 2(1), 36-45.
• Lee, J. J., Shim, J. C., & Ha, Y.H. (1994). Stereo correspondence using the Hopfield neural network of a new energy function. Pattern Recognition, 27(11), 1513-1522.
• Lillesand, T.M., Kiefer, R.W., & Chipman, J.W. (2008). Remote Sensing and Image Interpretation, John Wiley & Sons, 6th Edition, New York.
• Lu, D., & Weng, Q. (2007). A survey of image classification methods and techniques for improving classification performance. International journal of Remote sensing, 28(5), 823-870.
• Mather, P.M. (1987). Computer processing of remote-sensed images. John Wiley and Sons Ltd.
• Mathur A., Foody, G.M. (2008b). Crop classification by support vector machine with intelligently selected training data for an operational application. International Journal of Remote Sensing, 29, 2227–2240.
• Melgani, F., Bruzzone, L. (2004). Classification of hyperspectral remote sensing images with support vector machines. IEEE Transactıons on Geoscience and Remote Sensing, 42, 1778–1790.
• Nasr, M.S., Moustafa, M.A., Seif, H.A., & El Kobrosy, G. (2012). Application of Artificial Neural Network (ANN) for the prediction of EL-AGAMY wastewater treatment plant performance-EGYPT. Alexandria engineering journal, 51(1), 37-43.
• Orhan, O., Yalvac, S., & Ekercin, S. (2017). Investigation of Climate Change Impact on Salt Lake by Statistical Methods. International Journal of Environment and Geoinformatics, 4(1), 54-62.
• Öztemel, E. (2016). Yapay Sinir Ağları. Papatya Yayıncılık Eğitim, İstanbul, 230 s.
• Pal, M., Mather, P.M. (2003). An assessment of the effectiveness of decision tree methods for land cover classification. Remote Sensing of Environment, 86, 554-565.
• Pavuluri, M.K., Ramanathan, S., & Daniel, Z. (2002). A rule-based classifier using classification and regression tree (CART) approach for urban landscape dynamics. In Proceedings of International Geoscience and Remote Sensing Symposium, June (pp. 24-28).
• Pierce, L.E., Sarabandi, K., & Ulaby, F.T. (1994). Application of an artificial neural network in canopy scattering inversion. REMOTE SENSING, 15(16), 3263-3270.
• Pizzolato, A.N., Haertel, V. (2003). On The Application of Gabor Filtering in Supervised Image Classification, International Journal of Remote Sensing, 24, 2167-3189.
• Ridd, M. K., & Liu, J. (1998). A comparison of four algorithms for change detection in an urban environment. Remote sensing of environment, 63(2), 95-100.
• Rokni, K., Ahmad, A., Selamat, A., & Hazini, S. (2014). Water feature extraction and change detection using multitemporal Landsat imagery. Remote sensing, 6(5), 4173-4189.
• Şahin, M. (2012). Modelling of air temperature using remote sensing and artificial neural network in Turkey. Advances in space research, 50(7), 973-985.
• Simard, M., Saatchi, S.S., & De Grandi, G. (2000). The use of decision tree and multiscale texture for classification of JERS-1 SAR data over tropical forest. IEEE Transactions on Geoscience and Remote Sensing, 38(5), 2310-2321.
• Sun, F., Sun, W., Chen, J., & Gong, P. (2012). Comparison and improvement of methods for identifying waterbodies in remotely sensed imagery. International journal of remote sensing, 33(21), 6854-6875.
• T.C Resmi Gazete, 20 Ocak 2013, Sayı, 4153 https://www.resmigazete.gov.tr/eskiler/2013/01/20130120-9.htm
• Torun, A. T. (2015). Yapay arı koloni algoritmasının tarım alanlarının sınıflandırılmasında kullanılabilirliğinin irdelenmesi (Master thesis) Aksaray University, Aksaray, Turkey.
• Vapnik, V.N. (1995). The nature of statistical learning theory. New York, USA: Springer-Verlag.
• Viotti, P., Liuti, G., & Di Genova, P. (2002). Atmospheric urban pollution: applications of an artificial neural network (ANN) to the city of Perugia. Ecological Modelling, 148(1), 27-46.
• Walker, N.P., Eglen, S. J., & Lawrence, B. A. (1994). Image compression using neural networks. GEC journal of research, 11(2), 66-75.
• Work, E. A., & Gilmer, D. S. (1976). Utilization of satellite data for inventorying prairie ponds and lakes. Photogrammetric Engineering and Remote Sensing, 42(5), 685-694.
• Xu, M., Watanachaturaporn, P., Varshney, P.K., & Arora, M.K. (2005). Decision tree regression for soft classification of remote sensing data. Remote Sensing of Environment, 97(3), 322-336.
• Zhou, Q., Robson, M. (2001). Automated Rangeland Vegetation Cover and Density Estimation Using Ground Digital Images and a Spectral–Contextual Classifier, International Journal of Remote Sensing, 22 (17), 3457–3470.