آمار
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انجام یک مرحله پیش پردازش قبل از مرحله استخراج ویژگی در طبقه بندی داده های تصاویر ابر طیفی | ||
| نشریه دانشکده فنی | ||
| مقاله 6، دوره 42، شماره 3 - شماره پیاپی 1890، تابستان 1387 اصل مقاله (376.38 K) | ||
| نویسندگان | ||
بهرام صالحی؛ محمدجواد ولدان زوج؛ محمدرضا سراجیان 
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| کلیدواژه‌ها | ||
| استخراج ویژگی؛ الگوریتم Projection Pursuit؛ پیش پردازش؛ تصاویر ابر طیفی؛ طبقه بندی؛ فیلتر پایین گذر | ||
| عنوان مقاله [English] | ||
| A Preprocessing Stage before the Feature Extraction Procedure in Classification of Hyperspectral Images Data | ||
| چکیده [English] | ||
| Hyperspectral data potentially contain more information than multispectral data because of their higher spectral resolution. However, the stochastic data analysis approaches that have been successfully applied to multispectral data are not as effective for hyperspectral data as well. Various investigations indicate that the key problem that causes poor performance in the stochastic approaches to hyperspectral data classification is inaccurate class parameters estimation. It has been found that the conventional approaches can be retained if a preprocessing stage is established before feature extraction procedure in classification of hyperspectral data. For preprocessing stage it has been proposed two steps in this paper including dimensionality reduction and class separability improvement. Sequential Parametric Projection Pursuit was used for dimensionality reduction because of its special characteristics. Projection Pursuit algorithm performs the computation of class parameter estimation at a lower dimensional space, giving better parameter estimation. For class separability improvement a lowpass filter has been used after dimensionality reduction. This paper shows that for different number of features, classification accuracy is improved when the preprocessing stage is applied. | ||
| کلیدواژه‌ها [English] | ||
| Classification, Dimensionality Reduction, Feature extraction, hyperspectral Projection Pursuit Lowpass Filtering | ||
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آمار تعداد مشاهده مقاله: 1,478 تعداد دریافت فایل اصل مقاله: 3,252 |
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