کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6949652 | 1451281 | 2014 | 11 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Novel Folded-PCA for improved feature extraction and data reduction with hyperspectral imaging and SAR in remote sensing
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
سیستم های اطلاعاتی
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: Novel Folded-PCA for improved feature extraction and data reduction with hyperspectral imaging and SAR in remote sensing Novel Folded-PCA for improved feature extraction and data reduction with hyperspectral imaging and SAR in remote sensing](/preview/png/6949652.png)
چکیده انگلیسی
As a widely used approach for feature extraction and data reduction, Principal Components Analysis (PCA) suffers from high computational cost, large memory requirement and low efficacy in dealing with large dimensional datasets such as Hyperspectral Imaging (HSI). Consequently, a novel Folded-PCA is proposed, where the spectral vector is folded into a matrix to allow the covariance matrix to be determined more efficiently. With this matrix-based representation, both global and local structures are extracted to provide additional information for data classification. Moreover, both the computational cost and the memory requirement have been significantly reduced. Using Support Vector Machine (SVM) for classification on two well-known HSI datasets and one Synthetic Aperture Radar (SAR) dataset in remote sensing, quantitative results are generated for objective evaluations. Comprehensive results have indicated that the proposed Folded-PCA approach not only outperforms the conventional PCA but also the baseline approach where the whole feature sets are used.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: ISPRS Journal of Photogrammetry and Remote Sensing - Volume 93, July 2014, Pages 112-122
Journal: ISPRS Journal of Photogrammetry and Remote Sensing - Volume 93, July 2014, Pages 112-122
نویسندگان
Jaime Zabalza, Jinchang Ren, Mingqiang Yang, Yi Zhang, Jun Wang, Stephen Marshall, Junwei Han,