بکارگیری اطلاعات متنی جهت آشکارسازی نظارت نشده تغییرات از تصاویر ماهواره ای چندزمانه SAR مبتنی بر ادغام خوشه بندی و مدل سطوح همتراز

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشگاه خواجه نصیرالدین طوسی

2 دانشگاه جامع امام حسین(ع)

چکیده

در پژوهش حاضر چارچوبی جهت آشکارسازی نظارت‌نشده تغییرات، با استفاده از تصاویر چندزمانه SAR با بکارگیری اطلاعات متنی و مبتنی بر ادغام خوشه‌بندی و مدل سطوح هم‌تراز ارائه شده است. با بکارگیری اطلاعات متنی، همبستگی مکانی بین پیکسل‌ها در نظر گرفته شد و همچنین به منظور معرفی اتوماتیک تغییرات از روش پیشنهادی مبتنی بر ادغام خوشه‌بندی گوستافسون-کسل(GKC) و مدل سطوح هم‌تراز استفاده شد. استفاده از روش خوشه‌بندی موجب تولید منحنی اولیه با حداقل زمان همگرایی برای مدل سطوح هم‌تراز گردید و همچنین استفاده از مدل سطوح هم‌تراز موجب افزایش دقت تولید نقشه تغییرات با استفاده از فرآیند تکراری شد. نتایج نشان می‌دهد که نرخ خطای کل روش پیشنهادی نسبت به روش حدآستانه‌گذاری Otsu، خوشه‌بندی FCM و GKC و الگوریتم EM به میزان 62/0، 78/1، 70/1 و 48/4 برابر کاهش یافته است. نتایج حاصل، مؤید قابلیت بالای روش پیشنهادی جهت آشکارسازی نظارت‌نشده تغییرات تصاویر چندزمانه SAR است.

کلیدواژه‌ها

عنوان مقاله [English]

Unsupervised Change Detection Using Multi-temporal SAR Images Based on Improvement of Level Set Methods

نویسندگان [English]

  • Armin Moghimi 1
  • Safa Khazaei 2
  • Hamid Ebadi 1
1
2
چکیده [English]

In this research, a framework is presented for the unsupervised change detection using Multitemporal SAR images based on integration of Clustering and Level Set Methods. Spatial
correlation between pixels is considered by using contextual information. Furthermore, in the
proposed method integration of Gustafson-Kessel clustering techniques (GKC) and Level Set
Methods are used for change detection. Using clustering techniques has caused production of
the initial curve for LSM with a minimum convergence time and, as a result, use of LSM Leads
to increasing the accuracy of change map using the iterative process. In order to evaluate the
performance of the proposed method, this method is compared with some other existing stateof-the-art methods. The results show that the total error rate of the proposed method has been
reduced compared to these methods. Results show the high capability of the proposed method in
the unsupervised change detection of multi-temporal satellite SAR images.

کلیدواژه‌ها [English]

  • Unsupervised change detection
  • Contextual Information
  • Multitemporal SAR Images
  • Clustering
  • Level Set Methods

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رادار
دوره 4، شماره 1 - شماره پیاپی 1
651
اردیبهشت 1395
صفحه 57-68

فایل ها

سابقه مقاله

  • تاریخ دریافت: 28 آبان 1394
  • تاریخ بازنگری: 02 بهمن 1402
  • تاریخ پذیرش: 28 شهریور 1397
  • تاریخ انتشار: 01 اردیبهشت 1395

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