هیدروفیزیک

هیدروفیزیک

مقایسه‌ الگوریتم‌های کنی، منطق فازی و طبقه‌بندی نظارت شده به روش جنگل تصادفی در استخراج خطوط ساحلی (مطالعه‌ موردی بندر لاور)

نوع مقاله : مقاله پژوهشی

نویسندگان
دانشکده مهندسی عمران، آب و محیط زیست، دانشگاه شهید بهشتی، تهران، ایران
چکیده
تعیین موقعیت خط ساحلی و کمی‌سازی آن یکی از مهمترین برنامه‌های مهندسی و مدیریت مناطق ساحلی محسوب می‌شود. از این رو مدیریت سواحل نیاز به روش‌های نوین، سریع و مقرون به صرفه برای پایش دائمی سواحل دارد چرا که روش‌های مستقیم نقشه برداری ساحلی، بسیار وقت‌گیر و پرهزینه بوده و نمی‌تواند تامین کننده نیاز به پایش‌های مداوم خطوط ساحلی باشد. به این منظور، داده‌های سنجش از دور با کمترین هزینه، کوتاه‌ترین زمان، در گستره‌ی جغرافیایی وسیع‌تری راهکاری مناسب در این راستا می‌باشد. یکی از روش‌های مقرون به صرفه در این راستا، استفاده از روش‌های نوین پردازش تصاویر ماهواره‌ای می‌باشد. لذا هدف از این مطالعه، استخراج خطوط ساحلی با استفاده از الگوریتم کنی، منطق فازی و طبقه‌بندی نظارت شده با رویکرد شیءگرایی و مقایسـه‌ دقت آنهـا در تشخیص خط ساحلی است، که به صورت موردی در بندر لاور انجام شد. برای این منظور از تصاویر ماهواره Sentinel-2A استفاده شد. سپس با در نظر گرفتن ناحیه مرطوب/خشک به عنوان نماینده خط ساحلی، لبه‌یابی با استفاده از الگوریتم کنی، منطق فازی و طبقه‌بندی با رویکرد شیءگرایی به روش جنگل تصادفی روی تصویر انجام و خط ساحلی بندر لاور از هر سه روش استخراج شد، نهایتاً به منظور صحت سنجی نتایج، خط ساحلی حاصل از پردازش تصویر با استفاده از هر سه روش، با خط ساحلی رقومی سازی شده به وسیله‌ی کاربر خبره، مقایسه شد. که به ترتیب دقت حاصل از روش کنی 88%، منطق فازی90% و طبقه‌بندی شیءگرا 93% بود و نتایج بیانگر عملکرد مناسب جنگل تصادفی در مقایسه با سایر الگوریتم‌ها بود.
کلیدواژه‌ها

موضوعات


عنوان مقاله English

Comparison of Canny, Fuzzy Logic, and Supervised Random Forest Classification for Coastline Extraction (Case Study: Bandar Laver)

نویسندگان English

Fateme Bagheri
Asghar Milan
Alireza Vafaeinejad
Mahmood Piroznya
Faculty of Civil, Water and Environmental Engineering Shahid Beheshti University, Tehran, Iran
چکیده English

Iran, with more than six thousand kilometers of coastline along the Caspian Sea, Persian Gulf, and Gulf of Oman, ranks among the leading countries worldwide in terms of coastal extent and thus requires effective coastal monitoring and management. Determining and quantifying the coastline position is one of the most important tasks in coastal engineering and management programs. Traditional, field-based shoreline mapping is time-consuming and expensive and cannot meet the demand for continuous monitoring, so there is a growing need for fast, low-cost, and large-area methods such as satellite remote sensing. One effective solution is the extraction of coastlines from satellite imagery using advanced image processing and classification techniques.​ The aim of this study is to extract the coastline of Bandar Laver using three approaches—Canny edge detection, fuzzy logic, and supervised object-oriented classification with the random forest algorithm—and to compare their accuracy in shoreline identification. Sentinel‑2A satellite images were used, and the wet/dry boundary was taken as a proxy for the coastline. Edge detection was applied using the Canny algorithm, while fuzzy logic and object-oriented classification with random forest were implemented to generate shoreline maps, from which the coastline of Bandar Laver was derived for all three methods.​ For validation, the shorelines obtained from the image processing methods were compared with a reference shoreline digitized by an expert interpreter. The overall accuracies were 88% for the Canny method, 90% for the fuzzy logic approach, and 93% for the object-oriented random forest classification, demonstrating the superior performance of the random forest method for coastline extraction in the study area. These results confirm the suitability of object-based machine learning techniques for operational, cost-effective coastal monitoring in Iran.

کلیدواژه‌ها English

Sentinel images
Canny's algorithm
fuzzy logic
segmentation
object-oriented approach
random forest algorithm
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  • تاریخ دریافت 28 بهمن 1403
  • تاریخ بازنگری 20 اسفند 1403
  • تاریخ پذیرش 19 فروردین 1404