دانلود مقاله ISI انگلیسی شماره 79081
عنوان فارسی مقاله

الگوریتم های خوشه بندی فازی برای تشخیص تغییر بدون نظارت در تصاویر سنجش از دور

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
79081 2011 17 صفحه PDF سفارش دهید محاسبه نشده
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
Fuzzy clustering algorithms for unsupervised change detection in remote sensing images
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Information Sciences, Volume 181, Issue 4, 15 February 2011, Pages 699–715

کلمات کلیدی
سنجش از دور؛ تشخیص تغییر؛ تصاویر چند زمانه؛ خوشه بندی فازی؛ خوشه بندی c-means فازی؛ خوشه بندی Gustafson–Kessel؛ الگوریتم های ژنتیکی؛ بازپخت شبیه سازی شده - اندازه گیری اعتبار Xie–Beni
پیش نمایش مقاله
پیش نمایش مقاله الگوریتم های خوشه بندی فازی برای تشخیص تغییر بدون نظارت در تصاویر سنجش از دور

چکیده انگلیسی

In this paper, we propose a context-sensitive technique for unsupervised change detection in multitemporal remote sensing images. The technique is based on fuzzy clustering approach and takes care of spatial correlation between neighboring pixels of the difference image produced by comparing two images acquired on the same geographical area at different times. Since the ranges of pixel values of the difference image belonging to the two clusters (changed and unchanged) generally have overlap, fuzzy clustering techniques seem to be an appropriate and realistic choice to identify them (as we already know from pattern recognition literatures that fuzzy set can handle this type of situation very well). Two fuzzy clustering algorithms, namely fuzzy c-means (FCM) and Gustafson–Kessel clustering (GKC) algorithms have been used for this task in the proposed work. For clustering purpose various image features are extracted using the neighborhood information of pixels. Hybridization of FCM and GKC with two other optimization techniques, genetic algorithm (GA) and simulated annealing (SA), is made to further enhance the performance. To show the effectiveness of the proposed technique, experiments are conducted on two multispectral and multitemporal remote sensing images. A fuzzy cluster validity index (Xie–Beni) is used to quantitatively evaluate the performance. Results are compared with those of existing Markov random field (MRF) and neural network based algorithms and found to be superior. The proposed technique is less time consuming and unlike MRF does not require any a priori knowledge of distributions of changed and unchanged pixels.

خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.