کد مقاله کد نشریه سال انتشار مقاله انگلیسی ترجمه فارسی نسخه تمام متن
4464578 1313835 2016 16 صفحه PDF ندارد دانلود کنید
عنوان انگلیسی مقاله
Water-Body types identification in urban areas from radarsat-2 fully polarimetric SAR data
ترجمه فارسی عنوان
شناسایی انواع پهنه آبی در مناطق شهری از اطلاعات SAR پلاریمتری کامل RADARSAT-2
کلمات کلیدی
PolSAR؛ طبقه بندی نظارت شده. استخراج پهنه آبی؛ شناسایی انواع پهنه آبی
موضوعات مرتبط
مهندسی و علوم پایه علوم زمین و سیارات کامپیوتر در علوم زمین
چکیده انگلیسی


• Spatial contextual information help removing false alarms in built-up areas from the water-body extraction result using Wishart-ML classifier.
• Shape features are useful in classifying different water-body type but fail in distinguishing several other objects from water-body.
• Polarimetric features eliminate the misclassifications between water-body and other objects.
• The proposed water-body type classification framework provides satisfactory results both at the object level and the pixel level using different datasets.

This paper presents a novel method for supervised water-body extraction and water-body types identification from Radarsat-2 fully polarimetric (FP) synthetic aperture radar (SAR) data in complex urban areas. First, supervised water-body extraction using the Wishart classifier is performed, and the false alarms that are formed in built-up areas are removed using morphological processing methods and spatial contextual information. Then, the support vector machine (SVM), the classification and regression tree (CART), TreeBagger (TB), and random forest (RF) classifiers are introduced for water-body types (rivers, lakes, ponds) identification. In SAR images, certain other objects that are misclassified as water are also considered in water-body types identification. Several shape and polarimetric features of each candidate water-body are used for identification. Radarsat-2 PolSAR data that were acquired over Suzhou city and Dongguan city in China are used to validate the effectiveness of the proposed method, and the experimental results are evaluated at both the object and pixel levels. We compared the water-body types classification results using only shape features and the combination of shape and polarimetric features, the experimental results show that the polarimetric features can eliminate the misclassifications from certain other objects like roads to water areas, and the increasement of classification accuracy embodies at both the object and pixel levels. The experimental results show that the proposed methods can achieve satisfactory accuracies at the object level [89.4% (Suzhou), 95.53% (Dongguan)] and the pixel level [96.22% (Suzhou), 97.95% (Dongguan)] for water-body types classification, respectively.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: International Journal of Applied Earth Observation and Geoinformation - Volume 50, August 2016, Pages 10–25
نویسندگان
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