کاربرد تجزیه و تحلیل طیف مخلوط نرمال شده (NSMA) جهت استخراج مناطق ساخته شده شهری و استفاده از آن در شبکه عصبی مصنوعی (MLP) برای پیش بینی رشدآتی شهر

نوع مقاله: مقاله پژوهشی

نویسندگان

1 دانشجوی کارشناسی ارشد سنجش از دور و سیستم اطلاعات جغرافیایی دانشکده جغرافیای دانشگاه تهران

2 استادیار گروه سنجش از دور و سیستم اطلاعات جغرافیایی دانشکده جغرافیای دانشگاه تهران

چکیده

استفاده از تصاویر ماهوارهای با قدرت تفکیک مکانی متوسط به منظور شناسایی، نظارت و پیشبینی مناطق ساخته شده شهری در دهههای اخیر توسعه یافته است. مهمترین گام در پیشبینی رشد مناطق شهری، استخراج ویژگیهای سطح شهر با دقت و صحت بالا و مهمترین چالش در این راه پیچیدگی عوارض شهری و مسئله پیکسلهای مخلوط است. هدف از این تحقیق استفاده از مدلهای تجزیه و تحلیل زیر پیکسل، برای استخراج عوارض سطحی شهر رشت به منظور پیشبینی برای تغییرات رشد آتی این شهر است. بدین منظور از سه تصویر لندست مربوط به سالهای؛ 0991 (سنجنده TM)، 2002 (سنجنده +ETM) و5102 (سنجنده OLI/TIRS) و روش تجزیه و تحلیل طیف مخلوط نرمال شده (NSMA)، برای استخراج عوارض سطحی استفاده شد. برای طبقه بندی تصاویر از لایههای کسری پوشش به عنوان لایههای ورودی و عضوهای پایانی به عنوان نمونههای آموزشی و الگوریتم حداکثر احتمال به عنوان الگوریتم طبقهبندیکننده استفاده شد؛ که در نتیجه صحت کلی بالای 99%و ضریب کاپای بالای 89/0 برای تصاویر سه دوره بدست آمد. به منظور پیشبینی رشد شهری با شبکه عصبی در این تحقیق از مدل پرسپترون چند لایه(MLP)با الگوریتم یادگیری پس انتشار (BP) استفاده شد. نتایج مقایسه خروجی مدل با نقشه طبقهبندی سال 5102 ، ضریب کاپای 29%،کاپای استاندارد 98%  و کاپای طبقهای (برای طبقه ساخته شده) 39%، را نشان داد. مدل استفاده شده در این تحقیق در پیش بینی رشد مرزهای شهر موفق عمل کرده است، اما در پیشبینی مناطق ساخته شده انفرادی اطراف شهر صحت کمتری دارد.

کلیدواژه‌ها


عنوان مقاله [English]

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

نویسندگان [English]

  • Bahram Jomeh Zadeh 1
  • Sirus Hashemi 1
  • Ali Darvishi Bolourani 2
  • Majid Kiavarz 2
1 Ms student of remote sensing and GIS, faculty of geography, University of Tehran
2 Assistant professor of remote sensing and GIS, faculty of geography, University of Tehran
چکیده [English]

Using satellite images with a medium spatial resolution to detect, monitor and predict urban built-up areas, has developed in recent decades. The most important step in predicting of the urban areas growth is extracting the urban features with a high precision but the greatest challenge in this way is the complexity of urban components and the issue of mixed pixels. The purpose of this research is using sub-pixel analysis to extract the surface features of Rasht city to predict the future growth of the city’s built-up areas changes. To achieve this purpose, we used three Landsat images related to; 1990 (Landsat Sensor TM), 2002 (Landsat Sensor ETM +) and 2015 (sensor OLI / TIRS) years and Normalized Spectral Mixture Analysis (NSMA). In order to classify the images, the fraction layers were used as input layers, andend members were used as training samples and maximum likelihood algorithm was used as classifying algorithm.  As a result, the overall accuracy of over99% and the kappa coefficient of over 89% were achieved for the images of three periods of study. In this research, however, in order to predict the urban growth by ANN model, Multilayer Perceptron (MLP) with Back-Propagation learning algorithm (BP) were used. The results of comparison between the model’s output and the classification map of 2015 showed a 92% kappa coefficient, an 89% standard Kappa and a 93% classification Kappa (for classes), respectively. The used model in this research has been successful in predicting the growth of urban boundaries, but less accurate in predicting the individual built-up areas around the urban areas.

کلیدواژه‌ها [English]

  • Sub-pixels analysis
  • Normalized Spectral Mixture Analysis (NSMA)
  • Urban growth predicting
  • Multilayer Perceptron
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