ارائه روشی تلفیقی مبتنی بر الگوریتم‌های طبقه‌بندی پارامتریک و غیر پارامتریک به منظور جداسازی پوشش‌های مختلف در جنگل‌های هیرکانی

نوع مقاله : علمی - پژوهشی

نویسندگان

دانشکده مهندسی نقشه برداری و اطلاعات مکانی، پردیس دانشکده‌های فنی، دانشگاه تهران

چکیده

تهیه نقشه‌های پوشش اراضی با دقت بالا، همواره یکی از اهداف مهم محققان در زمینه مدیریت اراضی بوده است. هدف از‌این پژوهش، ارائه روش نوینی جهت تهیه نقشه‌های کاربری اراضی با استفاده از پردازش تصاویر ماهواره‌ای بوده است. به همین منظور، از تصاویر ماهواره لندست 8، به عنوان تصویر پایه و نقشه مدل رقومی ارتفاعی (DEM)، داده‌های حاصل از تجزیه به عنوان مولفه‌­های اصلی و شاخص‌های طیفی جهت استخراج نقشه پوشش اراضی در منطقه مطالعاتی استفاده شد. پس از پیش‌پردازش‌ها و آماده‌سازی داده‌های مورد نیاز، اقدام به تهیه نمونه‌های آموزشی شد. در‌این پژوهش، نمونه‌های آموزشی در دو بخش به کار گرفته شدند؛ در بخش اول از آنها به عنوان ورودی، جهت طبقه‌بندی تصویر با الگوریتم‌های نظارت شده، حداکثر احتمال و ماشین بردار پشتیبان استفاده شد. در بخش دوم، به‌منظور طبقه‌بندی با روش درخت تصمیم گیری، از‌ این نمونه‌ها برای تعیین محدوده بازتاب طیفی هر پوشش در طیف امواج الکترومغناطیس (باندهای تصویر، PCA، شاخص‌های طیفی و DEM) استفاده شد. سپس با استفاده از ‌این داده‌ها و شروط دودویی درخت تصمیم‌گیری، هر پوشش مشخص و نقشه پوشش آن استخراج شد. پس از تهیه نقشه‌های ذکر شده، به منظور تلفیق نتایج طبقه‌بندی و حصول دقت بالاتر، از روش حداکثر رای‌گیری به منظور تهیه نقشه تلفیقی جدید پوشش اراضی منطقه استفاده شد. همچنین به منظور ارزیابی دقت نقشه‌های تولیدی، از پارامترهای آماری منتج از ماتریس ابهام شامل دقت کلی، ضریب کاپا، دقت کاربر و دقت تولیدکننده استفاده شد. بر اساس نتایج حاصله، روش تلفیقی با دقت کلی 37/93 درصد و ضریب کاپا 91/0 دارای بیشترین دقت بوده است. دقت کلی نقشه پوشش روش درخت تصمیم‌گیری، ماشین بردار پشتیبان و حداکثر احتمال نیز به ترتیب 61/89، 01/88 و 6/87 درصد بوده‌اند. با توجه به‌اینکه در طبیعت پوشش خالص، به ندرت مشاهده می‌شود و بیشتر پوشش‌ها به صورت ترکیبی وجود دارند، لذا بهتر است از روش‌های نوینی که همه ابعاد پدیده‌ها را پوشش می‌دهند استفاده شود. در‌این پژوهش، اطلاعات حاصل از طبقه‌بندی نظارت شده و همچنین اطلاعات حاصل از روش منطقی درخت تصمیم‌گیری با یکدیگر تلفیق شده و نتایج حاصله به خوبی، بیانگر بهبود دقت نهایی طبقه‌بندی بودند.

کلیدواژه‌ها

عنوان مقاله [English]

A hybrid classification method based on fusion of parametric and non-parametric classification algorithms for Landuse/Landcover map in Hirkani Forests

نویسندگان [English]

  • Mohammad Saadat
  • Reza Shahhoseini
College of Engineering, University of Tehran, Tehran, Iran
چکیده [English]

Preparation of proper land use maps has always been one of the important goals of researchers and policymakers. The aim of this study was to provide a new method for preparing land use maps using remotely sensed data and satellite data imagery. For this Purpose, we used Landsat 8 data, Digital Elevation Model (DEM), Principal Component Analysis (PCA), and Spectral Indices to extract land use map in the study area. After all required preprocessing, the training samples were provided. In this study, the training samples were utilized in two parts; in the first part they were used as inputs for image classification using supervised algorithms of maximum likelihood Classification (MLC) and support vector machine (SVM). In the second part, in order to applying Decision Tree Classification (DTC), these training samples were used to determine the spectral reflection of each end-member in the spectrum of electromagnetic waves (image bands, PCA, spectral indices, and DEM).Then, using these binary data and DTC, each end-member was identified and the Landuse/Landcover (LULC) map was extracted. In order to combine the classification results and achieve higher accuracy, the Majority Vote Classification (MVC) method was applied to prepare a new compilation of land use in the area. In order to evaluate the accuracy of produced maps, the statistical parameters extracted from the confusion matrix including overall accuracy, kappa coefficient, user and producer’s accuracy were utilized. According to the results, the combined method (MVC) with a total accuracy of 93.37% and kappa coefficient of 0.91 had the highest accuracy. The overall accuracy of the DTC, SVM, and MLC were 89.61, 88.01 and 87.6%, respectively. Due to the fact that in the nature most of the landuse are mixed and complicated, it would be better to use new methods that cover all aspects of the phenomena. In this research, the data extracted from the supervised classifications as well as the data derived from the DTC were combined and the results clearly illustrate the improvement of the final accuracy of the classification.

کلیدواژه‌ها [English]

  • Majority Vote Classification
  • Landuse
  • Classification Algorithm
  • Remote Sensing
  • Landsat 8

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