مقایسۀ کارآیی الگوریتم‌های‌ ماشین‌ بردار پشتیبان و حداکثر احتمال در آشکارسازی تغییرات کاربری اراضی (مطالعۀ موردی: حوضۀ آبخیز سیمینه‌رود)

نوع مقاله : علمی - پژوهشی

نویسندگان

1 کارشناس ارشد مهندسی آبخیزداری، دانشکدۀ منابع طبیعی دانشگاه تربیت مدرس، نور

2 استادیار گروه اقتصاد محیط‌زیست، دانشگاه آزاد اسلامی، علوم و تحقیقات، تهران

چکیده

با توجه به اینکه الگوریتم‌های متنوعی برای طبقه‌بندی تصاویر ماهواره‌ای در سنجش از دور توسعه یافته‌اند، انتخاب الگوریتم مناسب طبقه‌بندی در دستیابی به نتایج صحیح نقش بسیاری ایفا می‌کند. به همین منظور در پژوهش حاضر، با مقایسۀ کارآیی صحت طبقه‌بندی دو الگوریتم حداکثر احتمال و ماشین‌های بردار پشتیبان، الگوریتم دقیق‌تر تعیین، و از آن برای بررسی روند تغییرات کاربری اراضی استفاده شد. تحقیق حاضر در حوزۀ آبخیز سیمینه‌رود و با استفاده از تصاویر سنجنده‌های TM، ETM+ و OLI انجام گرفت. نتایج تحقیق نشان داد که الگوریتم ماشین بردار پشتیبان، درمقایسه با الگوریتم حداکثر احتمال، تصاویر ماهواره‌ای را بهتر طبقه‌بندی کرده است و از میان کرنل‌های ماشین بردار، کرنل تابع پایۀ شعاعی (RBF) کارآیی بهتری داشته است. بنابراین، از الگوریتم ماشین بردار پشتیبان با کرنل تابع پایۀ شعاعی برای تهیۀ نقشۀ کاربری اراضی دوره‌های مورد بررسی و تغییرات کاربری استفاده شد. بررسی روند تغییرات کاربری اراضی، با استفاده از این کرنل، مشخص کرد که در طی دوره‌های بررسی‌شده، مساحت کاربری‌های زراعت آبی از 30.535 هکتار به 67.210 هکتار، زراعت دیم از 79.909 هکتار به 123.383 هکتار و مناطق مسکونی از 474 هکتار به 1934 هکتار افزایش یافته است درحالی‌که مراتع از 259.811 هکتار به 178.398هکتار، و منابع آب از 240 هکتار به 41 هکتار روند کاهشی دارند. 

کلیدواژه‌ها

عنوان مقاله [English]

Efficiency Comparison of Support Vector Machine and Maximum Likelihood Algorithms for Monitoring Land Use Changes

نویسندگان [English]

  • A Daneshi 1
  • M Panahi 2
1 M.Sc. of Watershed Management Engineering, Faculty of Natural Resources, Tarbiat Modares University, Nour
2 Assistant Prof., Science and Research Branch, Islamic Azad University, Tehran
چکیده [English]

Because the various algorithms have been developed for the land use classification by using remote sensing, the suitable algorithm selection plays an important role in achieving good results. For this purpose, by efficiency comparison of two algorithms classification i.e. support vector machines (SVM) and maximum likelihood (ML), the more precision method was determined and it was used for investigating land use changes trend. The present research was carried out using TM, ETM+ and OLI sensors images in Siminehroud watershed. The research results showed that SVM algorithm classified satellite images better than ML algorithm and radial basis function (RBF) kernel has the highest overall accuracy among the studied methods. Therefore, SVM algorithm with RBF kernel was used to derive land use maps and monitor land use changes in the studied periods. By analysis of land use changes trend using this kernel, it was found that during studied periods, irrigated farming from 30535ha to 67210ha, dry farming from 79909ha to 123387ha, residential from 474ha to 1934ha land uses have been increased but rangeland from 259811ha to 178397ha and water resources from 30535ha to 67210ha land uses are decreasing

کلیدواژه‌ها [English]

  • Satellite images
  • Support vector machines algorithm
  • Maximum likelihood
  • Land sue

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