مقایسه عملکرد شبکه عصبی مصنوعی، ماشین بردار پشتیبان و مدل شیءگرا در پایش تغییرات سطح پوشش برف با استفاده از تصاویر چند زمانه لندست (مطالعه موردی: کوهستان الوند)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشگاه پیام نور

2 دانشگاه تبریز

3 اداره کل هواشناسی استان همدان

4 شرکت آب منطقه‌ای استان همدان

چکیده

پوشش برف و تغییرات زمانی آن، از پارامترهای اساسی در بررسی­های هیدرولوژیکی و اقلیم شناسی می­باشند. امروزه با استفاده از تصاویر ماهواره­ای می­توان به ارزیابی تغییرات سطح پوشش برف در سری­های زمانی مختلف پرداخت. پژوهش حاضر با هدف پایش تغییرات سطح پوشش برف در کوهستان الوند همدان با استفاده از داده­های رقومی ماهواره لندست در سری­های زمانی سال­های 1975، 1986، 1993، 2001، 2008 و 2018 انجام گرفته است. روش تحقیق در این پژوهش، استفاده از طبقه­بندی شبکه عصبی مصنوعی، ماشین بردار پشتیبان و مدل شیء گرا جهت برآورد سطح پوشش برف بوده است که پس از انجام عملیات پیش پردازش بر روی تصاویر ماهواره­ای، نقشه­های طبقه­بندی سطح پوشش برف کوهستان الوند از روش­های شبکه عصبی، ماشین بردار پشتیبان و مدل شیءگرا تهیه گردید. سپس صحّت این روش­ها مورد ارزیابی قرار گرفت. پژوهش حاضر نشان داد به ترتیب، مدل شیءگرا، ماشین بردار پشتیبان و شبکه عصبی مصنوعی دارای بالاترین میزان دقت بودند، لذا تغییرات مساحت سطح پوشش برف در سری­های زمانی مختلف، با استفاده از روش شیءگرا محاسبه گردید. مساحت بدست آمده برای سطح پوشش برف در کوهستان الوند با استفاده از مدل شیءگرا به ترتیب عبارت بودند از، سال 1975 برابر با 630 کیلومتر مربع، سال 1986 برابر با 611 کیلومتر مربع، سال 1993 برابر با 414 کیلومتر مربع، سال 2001 برابر با 151 کیلومتر مربع، سال 2008 برابر با 242 کیلومتر مربع و سال 2018 برابر با 154 کیلومتر مربع که نشانگر کاهش چشمگیر سطح پوشش برف از سال 1975 تا سال 2018 در کوهستان الوند می­باشد.

کلیدواژه‌ها


عنوان مقاله [English]

Comparison performance of artificial neural network, support vector machine and object-oriented model for monitoring snow cover surface changes using Landsat multi temporal images (Case study: Alvand mountain)

نویسندگان [English]

  • Mostafa MousaPour 1
  • Bakhtiar Feizizadeh 2
  • Syed Akbar Hosseini 3
  • Hasan Kerchi 3
  • Seifi Azadeh 4
1 Payam Nour University
2 Tabriz University
3 Meteorological Administration of Hamedan Province
4 Water resource expert, Regional Water resource of Hamadan province.
چکیده [English]

Expanded abstract:
 snow is one of the most important forms of precipitation in hydrology cycle in mountainous basin which plays an important role on agricultural and domestic water supply resources as delayed flows in high flow seasons and minimal flow in low flow seasons and energy production. today, the use of remote sensing data is applied at obtaining the area of accurate snow cover data in the efficient management of water resources. The purpose of this study is to determine the changes in snow cover in alvand mountains of hamedan using of remote sensing. The research method is using of artificial neural network classification, support vector machine, and object oriented model, that with using of the most appropriate method among them has been calculated, the amount of snow cover area variations in different time series. Alvand mountain in hamadan province is located between hamedan, tuyserkan, asadabad and bahar. Its highest mountaintop, called alvand, is located 18 kilometers south of hamadan city and is 3584 meters above sea level. The direction of this mountain is drawn from the northwest to the southeast and the hamedan province divides into two northern and southern halves. The data used in this study include sensor images MSS, TM, ETM +, and OLI Landsat satellite in the time series of 1975, 1986, 1993, 2001, 2008, and 2018. To prepare a map of changes the snow cover area, was carried out processing operations on satellite images in three stages: preprocessing, processing, and post processing. Similar spectral separation and division of the class which has the same spectral behavior are called satellite information classification. The main purpose of classification of digital images is to create subject maps. In recent years, new approaches have been proposed to concurrent with the advancement of image computer processing technology, for example, the use of neural networks, tree decisions, and methods derived from fuzzy logic theory, the use of secondary information such as texture, background and ground effects are the most important of these methods. An artificial neural network algorithm is a method in the field of machine learning and artificial intelligence that eventuates from the human nervous system to analyze complex nonlinear models and parallel computing systems. One of the advantages of artificial neural networks is that they are independent of the assumption of statistical distribution. Neural networks are nonlinear and can transform the input data into a desired output as a complex mathematical function. Support vector machine is a sample classification method that first time was introduced by Vladimir vapnik. This method is a non-parametric supervised statistical method to classify the classes in the training data, super surface practice on them. The support vector machine is one of the supervised classification algorithms that predict every sample stand in which class or group. This algorithm has less sensitive to the phenomena of multidimensional space, for this reason, it is a suitable method for the classification of multi-spectral and hyperspectral data. One of the advantages of a support vector machine algorithm is to provide a good classified image resolution with small training samples. In recent years, many research has been carried out on the applications of fuzzy logic in remote sensing, have largely been based on object oriented methods, in addition to numerical values, is used the data of texture, shape and tone color, in classification process. The ability to classify the base pixels method is limited when different ground objects are recorded with the same numeric values on a digital image. The object oriented classification method has been proposed to solve this problem. One of the clearest difference between the basic image pixel processing and object oriented image processing are in processing of object oriented image, the processing basic units are image objects or segments, not single pixels, the other difference is that the classification in object-oriented image processing is soft classification, which is based on fuzzy logic. After operation preprocessing on satellite images, maps of classification the snow cover area was provided of this three mentioned method from alvand mountain. Then the validity of these methods was evaluated. This research specified that object oriented model, support vector machines and artificial neural network have the highest accuracy respectively and thus changes of snow cover area were calculated in different time series using the object-oriented method. The snow cover area obtained using of the object-oriented model were 630, 611, 414, 151, 242, 154 square kilometers in 1975, 1986, 1993, 2001 ,2008 and 2018 respectively, indicating the area of the snow cover have diminished significantly from 1975 to 2018 in the alvand mountains.

کلیدواژه‌ها [English]

  • Remote Sensing
  • artificial neural network
  • support vector machine
  • object base
  • alvand mountain
 
1. Akbari, E. and Shekari Badi, A. (2014). Processing and extracting information from satellite data using ENVI software, Mahvareh Publication. Tehran.
2. Alavipanah, S.K. Matinfar, H.R. Rafiei Emam, A. (2009), the Application of Information Technology in the Earth Sciences (On Digital Soil Mapping). University of Tehran Press. Tehran.
3. Arekhi, S. and Adibnejad, M. (2011), Efficiency assessment of the of Support Vector Machines for land use classification using Landsat ETM+ data (Case study: Ilam Dam Catchment).Iranian Journal of Rangeland and Desert Research.Vol.18, NO.3, PP.420-440. https://dx.doi.org/10.22092/ijrdr.2011.102175
4. Baseri Nam S, Esmaeily A, Dehghani M. (2015), Propose an Algorithm to Improve the Accuracy of Snow-Covered Mapping Using MODIS Images. Engineering Journal of Geospatial Information Technology Vol.3, No.1, PP.61-75. http://dx.doi.org/10.29252/jgit.3.1.61
  5. Blaschke, T. (2010), Object based image analysis for remote sensing, Journal of Photogrammetry and Remote Sensing, Vol. 65, No.1, PP. 2-16. https://doi.org/10.1016/j.isprsjprs.2009.06.004
  6. Daneshi, A. Vafakhah, M. and Panahi, M. (2016). Efficiency Comparison of Support Vector Machine and Maximum Likelihood Algorithms for Monitoring Land Use Changes. Iranian Journal of Remote Sensing & GIS, Vol.8, No.2, PP.73-86. http://jstnar.iut.ac.ir/article-1-889-fa.html
7.  Ebrahimi, H. Gheiby, A. Malakoti, H. (2012), Trend of Snow Cover Detection Using Satellite Data from MODIS over Snow-Rich Areas in Iran, Journal of NIVAR, Vol.36, PP.3-10.
8. Fatemi, S.B. and Rezaei, Y. (2014), Principles of Remote Sensing, Azadeh Publishers, Tehran.
9. Feizizadeh, B. Helali, H. (2010), Comparison Pixel-Based, Object-Oriented Methods and Effective Parameters in Classification Land Cover/ Land Use of West Province Azerbaijan, Physical Geography Research Quarterly, Vol.42, No.71, PP.73-84.
10. Ildoromi, A. Habibnejad Roshan, M. Safari Shad, M. Dalal Oghli. A. (2015), Application of MODIS Sensor and NDSI Index to Produce Snow Cover Map (Case Study of Bahar Watershed), Journal of Geographic Space, Vol.15, No.50, PP.125-140.
   11. Jafari, Gh. Hosseini, S.A.R. (2018), Leveling of Tourism Target Villages in the Slopes of Alvand Mountains in Hamadan Province, Quarterly Journal of Space Economics and Rural Development, Vol.6, PP.97-114. http://serd.khu.ac.ir/article-1-2979-fa.html
 12. Jomeh Zadeh, B. Hashemi, S. Darvishi Bolourani, A. Kiavarz, M. (2016), Application of Normalized Spectral Mixture Analysis (NSMA) to extract urban built-up areas and utilize it in artificial neural network (MLP) to predict the future growth of the city, Quarterly of Geographical Data (SEPEHR), Vol.24, No.96, PP.65-77. https://dx.doi.org/10.22131/sepehr.2016.18944
  13. Khosravi, M. Tavousi, T. Raeespour, K. Omidi Ghaleh mohammadi, M. (2017), A Survey on Snow Cover Variation in Mount Zardkooh-Bakhtyare Using Remote Sensing, Journal of Hydrogeomorphology, Vol.3, No.12, PP.25-44.
 14. Lu, L. Tao, Y. Di, Liping. (2018), Object Based Plastic Mulched Land Cover Extraction Using Integrated Sentinel-1 and Sentinel-2 Data, Journal of Remote Sensing, Vol.10, No.11, pp.1820-1837. https://doi.org/10.3390/rs10111820

15. Maithani, S. (2009), a neural network based urban growth model of an Indian city, Journal of the Indian Society of Remote Sensing, vol.37,PP.363-376.

16. Mir Yaghoobzadeh, M.H. and Ghanbarpour, M.R. (2010), Investigation to MODIS Snow Cover Maps Usage in Snowmelt Runoff Modeling (Case Study: Karaj Dam Basin), Journal of Geosciences, Vol.19, No.76, PP.141-148. https://dx.doi.org/10.22071/gsj.2010.55672
17. Mirmousavi, S.H. and Saboor, L. (2014), Monitoring the Changes of Snow Cover by Using MODIS Sensing Images at North West of Iran, Journal of Geography and Development, Vol.12, No.35, PP.181-200. https://dx.doi.org/10.22111/gdij.2014.1562
   18. Ojaghi, S. Ebadi, H. Farnood Ahmadi, F. (2015), Using artificial neural network for classification of high resolution remotely sensed images and assessment of its performance compared with statistical methods, American Journal of Engineering, Technology and Society, Vol.2, No.1, pp.1-8.
 19. Rasouli, A. A. (2014), Principles of Applied Remote Sensing Satellite Image Processing, Tabriz University Press, Tabriz.
20. Rasouli, A. A. and Mahmoudzadeh, H. (2010), Fundamental of Knowledge Based Remote Sensing, Elmiran Publishers, Tabriz.
  21.  Rayegani. B, Khajeddin, S. J. Soltani kopani, S. Barati, S. (2008), Analysis of MODIS Snow-Cover Map Changes during Missing Data Period. Journal of Water and Soil Science, Vol.12, No.44, PP.315-332.
22. Roshani, N. Valdan Zoj, M. J. Rezaei, Y. (2008), Snow Measurement Using Remote Sensing Data (Case Study: Alamchal Glacier Area), Geomatics Conference, Tehran, Iran Mapping Organization.
 23. Shojaeeian, A. Mokhtari Chelche, S. Keshtkar, L. Soleymani rad, E. (2015), Comparing the Performance of Parametric and Nonparametric Methods in Land Cover Classification using Landsat-8 Satellite Images (Case study: A part of Dezful city), Quarterly of Geographical Data (SEPEHR), Vol.24, No.93, PP.53-64. https://dx.doi.org/10.22131/sepehr.2015.14007
24. Solaimani, K. Darvishi, Sh. Shokrian, F. Rashidpour, M. (2018), Monitoring of temporal-spatial variations of snow cover using the MODIS image (Case Study: Kurdistan Province), Iranian Journal of Remote Sensing & GIS, Vol.10, No.3, PP.77-104.
25. Talebi Esfandarani, S. Alavipanah, S.K, Alimohammadi Sarab, A. Rosta, H. (2011). Cloud separation from snow in MODIS images, using Snow Map algorithm and cloud mask algorithm, Iranian Journal of Remote Sensing & GIS, Vol.3, No.1, PP.71-90.
 26.Yan, G. Mas, J. F. Maathuis, B.H.P. Xiangmin, Z. Van Dijk, P.M. (2006). Comparison of pixel based and object oriented image classification approaches (A case study in a coal fire area, Wuda, Inner Mongolia, China). International Journal of Remote Sensing Vol.27, No.18, PP. 4039-4055.https://doi.org/10.1080/01431160600702632