بهبود طبقه بندی منطقه شهری با استفاده از تلفیق تصاویر اپتیک چندباندی و لایدار با قدرت تفکیک مکانی بالا

نوع مقاله: مقاله پژوهشی

نویسندگان

1 کارشناس ارشد سنجش از دور، دانشکده مهندسی نقشه برداری و اطلاعات مکانی، پردیس دانشکده های فنی، دانشگاه تهران

2 کارشناس ارشد سنجش از دور و سیستم اطلاعات مکانی، دانشکده علوم جغرافیایی، دانشگاه خوارزمی تهران

3 دانشجوی کارشناس ارشد مدیریت شهری، دانشکده اقتصاد و مدیریت، دانشگاه آزاد اسلامی واحد شیراز

10.22131/sepehr.2019.35638

چکیده

امروزه با گسترش مناطق شهری تولید اطلاعات دقیق و به روز از جمله اطلاعات اساسی، به منظور مدیریت و برنامهریزی شهرها است. گسترش روز افزون تکنولوژی سنجش از دور امکان استخراج اطلاعات متنوع از پوششهای شهری را فراهم آورده که موجب جلب توجه محققهای فراوانی به این موضوع شده است. وجود عوارض متنوع و نیز کاربریهای مختلف اطلاعات مکانی مناطق شهری، تلفیق منابع داده مختلف به منظور شناسایی عوارض را به امری کاربردی مبدل کرده است. هدف این تحقیق تلفیق ویژگیهای بهینه استخراج شده از دادههای اپتیک و لایدار به منظور شناسایی عوارض شهری در منطقه مورد مطالعه میباشد. در این راستا ویژگیهای مختلفی از هر یک از این دادهها استخراج شده است. از جمله این ویژگیها میتوان به ویژگیهای رنگی، شاخص گیاهی و بافت از تصویر اپتیک و ویژگیهای نرمی، مدل ارتفاعی رقومی نرمال و زبری از تصویر لیدار اشاره نمود. سپس به منظور انتخاب ویژگیهای بهینه از الگوریتم ژنتیک استفاده شده است. در انتها با استفاده از روش طبقهبندی کننده ماشینبردار پشتیبان به شناسایی عوارض مورد نظر پرداخته شده است. دقت طبقهبندی کننده الگوریتم ماشین بردار پشتیبان در منطقه مورد مطالعه با استفاده از ویژگیهای بهینه و دادههای اولیه 734/88 محاسبه شده که نسبت به طبقهبندی داده اولیه اپتیک چندباندی دارای بهبود 438/25 درصدی و نسبت به طبقهبندی داده اولیه لایدار دارای بهبود 236/18 درصدی است. نتایج بررسی نشان دهنده افزایش دقت طبقهبندی با استفاده از ویژگیهای بهینه در کنار باندهای اولیه است.

کلیدواژه‌ها


عنوان مقاله [English]

Improving the classification of urban areas with the Fusion of multispectral optical and high spatial resolution LiDAR images

نویسندگان [English]

  • Alireza Arofteh 1
  • Taher Reza Mohammed 1
  • Ali Hossingholizade 2
  • Ehsan Hoghoghi fard 3
1 M.Sc. of Surveying and geospatial engineering, College of Engineering of University of Tehran
2 Department of Geographic Sciences, Kharazmi University
3 Department of urban management, Islamic Azad University of Shiraz
چکیده [English]

Increasing development of urban areas, the need for various information from the urban environment, and also technological advancements have increased the importance of automatic and semi-automatic classification and identification of this type of land cover. The diversity of remote sensing data have created a wide scope for urban feature detection. Moreover, by launching satellite sensors with a spatial resolving power of less than 1 meter, a dramatic revolution has occurred in the tendency of remote sensing researchers toward classification of urban features. The existence of various features and different applications of spatial information in urban areas have made it possible to integrate various data sources with the aim of identifying different urban features. The present study seeks to integrate optimal properties extracted from optical and LiDAR data in order to identify urban features in the study area. In this regard, colored features, normal difference vegetative index (NDVI), first-order statistical texture in three windows of 5×5, 7×7 and 9×9, second-order statistical texture in three windows of 7×7, 11×11 and 15×15 extracted from the multispectral optical data were calculated along with features of normalized difference index (NDI), slope, slope direction, profile curve, surface curve, roughness, variance, laplacian, smoothness and normalized digital surface model (nDSM) extracted from the LiDAR data. Since increased amount of information has made the process of identifying features in the region time-consuming, the present study applies intelligent genetic algorithm to select optimal features from the calculated features. A total number of 361 features were produced from this data, including 9 colored features, a vegetation index, 144 first-order statistical texture, and 192 second-order statistical texture from multispectral optical data and 14 features from LiDAR data. Then, 17 features including seven features of the LiDAR data and 10 features of the multispectral optical data were determined using genetic algorithm as the optimal features for more appropriate identification of urban features. Finally, support vector machine (SVM) classification method was used to identify the desired features. Results indicate that compared to LiDAR data, multispectral optical data have a better performance in classifying vegetation features, while LiDAR data have been more suitable for the classification of road and building features. In other words, multispectral optical data work appropriately in identifying features with different radiometric information, while classification of features with similar radiometric information, such as roads and buildings is problematic. Thus, LiDAR elevation data help in identification of these features. Additionally, using optimal features along with the primary bands have increased the accuracy of urban features classification. Using optimal features and initial data, the accuracy of support vector machine algorithm classifier in the study area is calculated to be 88.734, which shows 25.438% improvement compared to the initial multispectral optical data classification, and 18. 236% improvement compared to the initial LiDAR data classification.

کلیدواژه‌ها [English]

  • Urban area
  • Optical and LiDAR images
  • Feature level fusion
  • genetic algorithm
  • Support Vector Machine
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