کد مقاله کد نشریه سال انتشار مقاله انگلیسی ترجمه فارسی نسخه تمام متن
4464597 1313835 2016 10 صفحه PDF ندارد دانلود رایگان
عنوان انگلیسی مقاله
An efficient unsupervised index based approach for mapping urban vegetation from IKONOS imagery
ترجمه فارسی عنوان
یک روش کارآمد مبتنی بر شاخص بدون نظارت برای نقشه برداری پوشش گیاهی در مناطق شهری از تصاویر IKONOS
کلمات کلیدی
طبقه بندی نظارت نشده؛ شاخص پوشش گیاهی؛ وقفه های طبیعی جنکس ؛ خوشه بندی سلسله مراتبی؛ ماشین بردار پشتیبانی؛ تصاویر IKONOS
Unsupervised classification; Vegetation index; Jenks natural breaks; Hierarchical clustering; Support vector machine; IKONOS imagery
موضوعات مرتبط
مهندسی و علوم پایه علوم زمین و سیارات کامپیوتر در علوم زمین
چکیده انگلیسی


• There is a potential for a completely unsupervised mapping of urban vegetation cover from an IKONOS-derived vegetation Index.
• Two unsupervised techniques are investigated for extraction of urban trees/shrubs and ground vegetation cover types.
• Techniques investigated include the Jenks natural breaks classification and an innovative two-step clustering technique.
• The unsupervised techniques are simple, efficient and can be easily automated for use in low resource areas.

Despite the increased availability of high resolution satellite image data, their operational use for mapping urban land cover in Sub-Saharan Africa continues to be limited by lack of computational resources and technical expertise. As such, there is need for simple and efficient image classification techniques. Using Bamenda in North West Cameroon as a test case, we investigated two completely unsupervised pixel based approaches to extract tree/shrub (TS) and ground vegetation (GV) cover from an IKONOS derived soil adjusted vegetation index. These included: (1) a simple Jenks Natural Breaks classification and (2) a two-step technique that combined the Jenks algorithm with agglomerative hierarchical clustering. Both techniques were compared with each other and with a non-linear support vector machine (SVM) for classification performance. While overall classification accuracy was generally high for all techniques (>90%), One-Way Analysis of Variance tests revealed the two step technique to outperform the simple Jenks classification in terms of predicting the GV class. It also outperformed the SVM in predicting the TS class. We conclude that the unsupervised methods are technically as good and practically superior for efficient urban vegetation mapping in budget and technically constrained regions such as Sub-Saharan Africa.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: International Journal of Applied Earth Observation and Geoinformation - Volume 50, August 2016, Pages 211–220
نویسندگان
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