شناسایی و طبقه‌بندی نیمه‌اتوماتیک بافت‌های مدرن و فرسودۀ شهری براساس الگوهای طیفی و مکانی در محیط سنجش‌ازدور شیءگرا (مطالعۀ موردی: شهر اصفهان)

نوع مقاله: مقاله علمی پژوهشی

نویسندگان

1 استادیار گروه سنجش‌ازدور و سیستم اطلاعات جغرافیایی، دانشگاه تبریز

2 کارشناس ارشد سنجش‌ازدور و سیستم اطلاعات جغرافیایی، دانشگاه تبریز

چکیده

الگوی توسعة محله‌های شهری به‌موازات رشد و گسترش شهر، دستخوش تحولات زیادی در گذر زمان شده است. این تغییرات در شهرهایی که پیشینة تاریخی دارند مشهودتر است؛ به‌گونه‌ای که می‌توان تفاوت بارزی میان محله‌های قدیمی که بدون برنامه ایجاد و پس از شکل‌گیری مشمول طرح و برنامه شده‌اند با محله‌های جدید از پیش برنامه‌ریزی‌شده مشاهده کرد. یکی از فنونی که می‌تواند در بررسی این تفاوت و مقایسة تطبیقی الگوی محله‌ها قدیمی و جدید در مطالعات شهری استفاده شود، فناوری سنجش‌ازدور و پردازش شیءگرا است. پردازش شیءگرا نوعی پردازش تصویر است که به‌دلیل استفاده از اطلاعات هندسی، محیطی و محتوای تصاویر می‌تواند دستیابی به‌دقت بالا را در فرایند طبقه‌بندی شیءگرا میسر کند. هدف از این مطالعه، مقایسة تطبیقی الگوهای بافت مدرن و سنتی شهر اصفهان با استفاده از فرایند پردازش شیءگرا و نرم‌افزار eCognition است. برای نیل به این هدف دو محلة شهری انتخاب شد و مراحل پژوهش روی آن‌ها صورت گرفت. در راستای استخراج الگوهای بافت شهری، انواع تکنیک‌های پردازش شیءگرا شامل اطلاعات هندسی، بافت، ضریب تراکم، نامنظمی اشکال و... به‌کار گرفته شد تا الگوهای شیءپایة تصاویر ماهواره‌ای برای شناسایی بافت سنتی و مدرن استخراج شود. پس از شناسایی الگوریتم‌های مناسب، طبقه‌بندی فازی با الگوریتم نزدیک‌ترین همسایه اعمال شد سپس ارزیابی نتایج با مطالعات میدانی صورت گرفت. ارائة روش‌هایی نوین برای شناسایی و طبقه‌بندی بافت شهری در نتایج پژوهش حاضر می‌تواند راهگشای انواع مطالعات باشد تا الگوهای بافت در محیط‌های شهری به‌سرعت شناسایی شود.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

A Semi-Automated Approach for Identifying and Classifying Urban Old and Modern Textures Based on Spectral and Spatial Patterns in Object-Oriented Remote Sensing (Case Study: Isfahan City)

نویسندگان [English]

  • Bakhtiar Feizizadeh 1
  • Saleheh Kazemi 2
  • Samira Sahrafei 2
1 Assistant Professor of Remote Sensing and GIS, University of Tabriz, Iran
2 MA in Remote Sensing and GIS, University of Tabriz, Iran
چکیده [English]

Introduction
Urban environment has been facing with significant changes in terms of land use/cover (LULC) over time. Accurate and up-to-date data describing LULC changes can promote such studies. They can be applied to quantify the amount of rural to urban changes, identify change trajectories, study legacy effects, help understand how change is occurring, and  predict future changes. In terms of urban change detection and monitoring LULC, the remote sensing technology is known as a very effective methodology for monitoring urban environments and LULC changes. There are several approaches for processing remote sensing satellite imagery such as pixel based and object based image analysis. An Object Based Image Analysis (OBIA) is considered as one of the well-established techniques for processing satellite images when applied to environmental monitoring of cities. Unlike pixel based approach, the OBIA make use of spectral information together with spatial characteristics of ground objects. Such specific ability allows effective modeling of ground objects. OBIA has gained prominence in the field of remote sensing over the last decade. This approach has the potential to overcome weaknesses associated with pixel based analysis in disregarding geometric and contextual information. When it is used within the “geo-domain” or at the scales related to earth “geo- centric” applications,, in scientific literature it is often referred to as geographic object-based image analysis (GEOBIA). OBIA is a knowledge-driven approach in which a range of diagnostic features for a particular object can be integrated on the basis of expert knowledge. This approach aims to represent the content of a complex scene in a manner that best describes the imaged reality by mimicking human perception. An integrated approach in OBIA allows us to incorporate spectral information (e.g., color) and spatial characteristics (e.g., size, shape), together with textural data and contextual information (e.g., association with neighboring objects), for modeling urban objectives effectively. Based on this statement, OBIA techniques can be used in the review and observation of the difference and adaptive comparison between the traditional and modern quarter pattern of the urban environments. In this regard, OBIA is known as effective and powerful image analysis processing method which helps obtain high accuracy satellite images.
Methodology
This research utilizes OBIA’s capabilities for modeling urban characteristics. The aim of this study is to compare textural-patterns of distressed and modern areas in Esfahan city by applying an object based approach. To achieve this goal, two categories of urban neighborhoods namely Nokhajo and Mardavij were selected from distressed and modern areas, respectively. The Quick Bird satellite images were acquired for the year 2015. In order to perform object based approach, the object based image processing started off by applying multi resolution segmentation based on spatial and spectral patterns of each area. Accordingly, object based methods are applied for identifying the spectral and spatial patterns of those areas. For this goal, the shape indexes were used as compactness for segmentation under specific scale parameters. The segmentation process was performed several times to obtain more accurate scale parameter. In order to extract the urban texture patterns, the rule based classification was performed by applying OBIA based algorithms consistent with physical and spectral characteristics of the urban objects. For this to happen, variety of OBIA techniques including geometrical information, texture, compression ratio, irregular shapes and etc were employed to derive spatial patterns of each part. The outcome of these OBIA algorithms was used to identify spatial patterns of distressed and modern zones. In doing so, after identifying the appropriate algorithms, fuzzy classification with nearest neighbor algorithm was applied for class modeling process. In terms of fuzzy rule based classification, the process was performed by employing fuzzy membership function as well as fuzzy operators. The memberships functions allow define the relationship between feature values and the degree of membership to a class using fuzzy logic. By comparing the membership degree achieved from membership function, the “AND” operator was selected to be effective operator for object based fuzzy classification.  Accordingly, fuzzy rule based classification was performed by employing “AND” operator and applying textural, shape, geometric, statistical, spatial and spectral indices. In order to assess the accuracy of results, the accuracy assessment process was done based on data gathered in field operation. The error matrix and kappa coefficient were derived by comparing the ground truth dataset and results of classifications.
Results and discussion
Results of this research indicated that OBIA is indeed an effective method for modeling urban structure and classifying the urban objects based on characteristic of each item. According to the results, integration of spectral and spatial patterns leads to effective modeling of urban structure. Our research results also confirmed that textural algorithms lead to detection of urban component. Well organized road network system together with distribution of green space and normal density in building were identified as the most important indicators in modern part of Esfahan. However, very high density in building, less green space area with narrow road network systems were observed as spatial characteristics of the distressed area. According to this statement, OBIA represents very effective and powerful methodology for modeling urban structure by means of integration of spectral and spatial characteristics.
Conclusion
Within this research, we present a novel methodology for comparing the different structure of urban environment based on object based remote sensing. Since we have carried out a comprehensive analysis for capability of each object algorithm, the results of this research are important for identifying and classifying urban texture patterns. The archived results can be used in rapid identification of texture patterns in urban environments and is useful to a variety of urban planning studies. The proposed approach in this research will support researchers/students to employ effective algorithms in OBIA which lead to obtain more accurate results. The results are also important for regional governmental departments such as the Municipality of Esfahan for updating land use/cover maps which are the bases of any decision and planning. 

کلیدواژه‌ها [English]

  • urban texture patterns
  • segmentation
  • spatial and spectral algorithms
  • object-based image analysis
  • Isfahan City

اسحاقیان، فرانک، حسنی، عاطفه و سید مسلم سیدالحسینی، 1391، ارزیابی طرح بهسازی و نوسازی قطاع 2 بافت پیرامون حرم مطهر با تأکید بر حوزة کالبدی-عملکردی، چهارمین کنفرانس برنامه‌ریزی و مدیریت شهری، مشهد، دانشگاه مشهد.

https://www.civilica.com/Paper-URBANPLANING04-URBANPLANING04_027.html،COI: URBANPLANING04_027.

2. ادیبی‌سعدی‌نژاد، فاطمه، 1389، مفهوم بافت فرسوده و ویژگی‌های آن، ماهنامة شوراها، سال پنجم، شمارة 54، صص 4-9.

3. حبیبی، کیومرث، پوراحمد، احمد و ابوالفضل مشکینی، 1386، بهسازی و نوسازی بافت‌های کهن شهری، انتشارات دانشگاه کردستان.

4. حسینی، سید مهدی، نجار اعرابی، بابک و حمید سلطانیان‌زاده، 1385، طبقه‌بندی تصاویر رنگی عنبیه به کمک ماتریس هم‌اتفاقی تصویر Hue: یک لایه از ساختار سلسله‌مراتبی برای شناسایی مقاوم افراد، چهارمین کنفرانس ماشین بینایی و پردازش تصویر ایران، مشهد، دانشگاه فردوسی مشهد.

https://civilica.com/Paper-ICMVIP04-ICMVIP04_020.html،COI: ICMVIP04_020.

5. رضایی‌مقدم، محمدحسین و همکاران، 1389، طبقه‌بندی پوشش اراضی/ کاربری اراضی براساس تکنیک شیءگرا و تصاویر ماهواره‌ای، مطالعة موردی: استان آذربایجان غربی، فصلنامة پژوهش‌های آبخیزداری، دورة بیست و سوم، شمارة 87، صص 20-35.

6. سلطان‌زاده، حسین، 1365، مقدمه‌ایبرتاریخشهرنشینیدرایران، نشر آبی.

7. مرکز آمار ایران، 1390، سرشماری عمومی نفوس و مسکن ایران.

8. فیضی‌زاده، بختیار و علیرضا طاهری، زیر چاپ، استفاده از تکنیک‌های پردازش شیءپایه در مدل‌سازی تغییرات پوشش و کاربری اراضی حاصل از رشد شهری در محدودة شهر مراغه، فصلنامة آمایش محیط، مقالة پذیرفته‌‌شده و در حال چاپ.

9. فیضی‌زاده، بختیار و سعید سلمانی، 1395، مدل‌سازی تخریب اراضی کشاورزی بر اثر رشد و توسعة شهری با به‌کارگیری روش‌های شیء پایة پردازش تصاویر ماهواره‌ای در محدودة شهری ارومیه، مجلة آمایش سرزمین، دورة هشتم، شمارة 2، صص 177-202.

10. فیضی‌زاده، بختیار، عزتی، سودابه و شهرام ملکی، 1394، کاربرد الگوریتم‌های طبقه‌بندی شیءگرای تصاویر ماهواره‌ای برای ارزیابی روند تغییرات کاربری اراضی (مطالعة موردی: شهرستان تبریز)، بیست و دومین همایش و نمایشگاه ملی ژئوماتیک، تهران، سازمان نقشه‌برداری کشور.

11. فیضی‌زاده، بختیار و حسین هلالی، 1389، مقایسة روش‌های پیکسل‌پایه، شیءگرا و پارامترهای تأثیرگذار در طبقه‌بندی پوشش/ کاربری اراضی استان آذربایجان غربی، مجلة پژوهش‌های جغرافیای طبیعی، دورة چهل و دوم، شمارة 71، صص 73-84.

12. قنبری، صدیقه و همکاران، 1390، پنهان‌شکنی در تصاویر با استفاده از ماتریس هم‌رخدادی و شبکة عصبی، نشریة مهندسی برق و مهندسی کامپیوتر ایران، سال نهم، شمارة 3، صص 169-174.

13. Alqurashi, A. F., Kumar, L. and Sinha, P., 2016, Urban Land Cover Change Modelling Using Time-Series Satellite Images: A Case Study of Urban Growth in Five Cities of Saudi Arabia, Journal of Remote Sensing, Vol. 8, No. 10, PP. 838-852; DOI: 10.3390/rs8100838.

14. Adibi Sadinejad, F., 2010, The Concept of Worn-out Texture and its Features, Journal of Shoraha, Vol. 5, No. 54, PP. 4-9. (In Persian)

15. Blaschke, T., Feizizadeh, B. and Hölbling, D., 2014, Object-Based Image Analysis and Digital Terrain Analysis for Locating Landslides in the Urmia Lake Basin- Iran, Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 7, No. 12, PP. 4806-4817; DOI: 10.1109/JSTARS.2014.2350036.

16. Blaschke, T., 2010, Object based image analysis for remote sensing, ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 65, No. 1, PP. 2-16.

17. Eshaghian, F., Hasani, A. and SeyedAlhoseini, M., 2012, Evaluation of the Improvement and Renovation of the Sector No. 2 Surrounding the Shrine Haram with Emphasis on the Physical and Functional Field, 4th Urban Planning and Management Conference, Mashhad University, Mashhad, Iran, 10 and 11 May; COI:  URBANPLANING04_027. (In Persian)

18. Feizizadeh, B., and Taheri, A., (Under Print), Use of Object-Based Processing Techniques in Modeling of Land-Use Changes from Urban Growth in the Maragheh City, Journal of Enviromental Based Territorial Planning. (In Persian)

19. Feizizadeh, B., and Salmani, S., 2016, Modeling Agricultural Destruction Lands Resulted by Urban Growing in Suburb of Urmia City by Applying an Object Based Image Analysis Approach, Journal of Town and Country Planning, Vol. 8, No. 2, PP. 177-202. (In Persian)

20. Feizizadeh, B., Ezati, S. and Maleki, S., 2015, Application of Object-Oriented Classification Algorithms Satellite Image to Assess the Land Use Change Process (Case Study: Tabriz), 22th National Geomatics Conference, Iran National Cartographic Center, 17-19 May. (In Persian)

21. Feizizadeh, B., and Helali, H., 2010, Comparison Pixel-Based, Object-Oriented Methods and Effective Parameters in Classification Land Cover/ Land Use of West Province Azerbaijan”, Journal of Physical Geography Research, Vol. 42, No. 71, PP. 73-84. (In Persian)

22. Grebby, S., Field, E., and Tansey, K., 2016, Evaluating the Use of an Object-Based Approach to Lithological Mapping in Vegetated Terrain,Journal of Remote Sensing, Vol. 8, No. 10, PP. 843-863; DOI: 10.3390/rs8100843.

23. Gebejes, A., and Huertas, R., 2013, Texture Characterization based on Grey-Level Cooccurrence Matrix, Conference of Informatics and Management Sciences, Slovakia, Zilina, 25-29 March.

24. Ghanbari, S. et al., H., 2011, Image analysis Method Based on Co-Occurrence Matrix and Neural Network, Journal of Electrical Engineering and Computer Engineering of Iran, Vol. 9, No. 3, PP. 169-174. (In Persian)

25. Habibi, K., PoorAhmad, A., and Meshkini, A., 2007, Improvement and Renovation of Olden Urban Texture, Kurdistan University Publication, Kurdistan, Iran. (In Persian)

26. Hoseini, M., Najar Arabi, B., and Soltanianzadeh, H., 2007, The Classification of Iris Color Images with the Co-Occurrence Matrix of Hue Image: A Layer of Hierarchical Structure for the Identification of Resistant Individuals, 4th Iraninan Conference on Machine Vision and Image Processing, Mashhad University, Mashhad, Iran, 14 and 15 Feb, 2007. (In Persian)

27. Harel, N. and Smith, T., 1997, A Texture Based Approach to Defect Analysis of Grapefruits, Georgia Institute of Technology, CS7321 Winter.

28. Haralick, R. M., Shanmugam, K. and Dinstein, I., 1973, Textural Features of Image Classification, IEEE Transactions on Systems, Man and Cybernetics, Vol. SMC-3, No. 6, PP. 610-621.

29. Pesaresi, M., Gerhardinger, A., and Kayitakire, F., 2008, A Robust Built-Up Area Presence Index by Anisotropic Rotation-Invariant Textural Measure, Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 1, No. 3, PP. 180-192; DOI: 10.1109/JSTARS.2008.2002869.

30. Rezaeimogadam, M. H. et al. 2010, Land Use/ Land Cover Classification Based on Object-Oriented Technique and Satellite Image (Case study: West Azerbaijan Provinces), Journal of Whatershed Management Research, Vol. 23, No. 87, PP. 20-35. (In Persian)

31. Statistical Center for Iran, 2011, Population and Housing Census of Iran. (In Persian)

32. Soltanzadeh, H., 1986, Introduction to Urban History in Iran, Abi Publication, Iran. (In Persian)

33. TabibMahmoudi, F., Samadzadegan, F., and Reinartz, P., 2015, “Context Aware Modification on the Object Based Image Analysis”, Journal of the Indian Society of Remote Sensing, Vol. 43, No. 4, PP. 709-717; DOI:10.1007/s12524-015.

34. Wezyk, P. et al., 2016, Using Geobia and Data Fusion Approach for Land Use and Land Cover Mapping, Journal of Questions Geographica, Vol. 35, No. 1, PP. 93-104; DOI: https://doi.org/10.1515/quageo-2016-0009.