کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4969935 | 1449988 | 2016 | 33 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Spectral-spatial hyperspectral image ensemble classification via joint sparse representation
ترجمه فارسی عنوان
طبقه بندی گروهی تصویر هیپراکترال طیفی-فضایی از طریق نمایش مجردی مشترک
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کلمات کلیدی
طبقه بندی، یادگیری گروهی تصاویر پرترافیک، بازیابی ضعیف مشترک، همبستگی فضایی،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
چشم انداز کامپیوتر و تشخیص الگو
چکیده انگلیسی
Ensemble learning can improve the performance of classification by integrating a set of classifiers, and shows significant potential benefits to the classification of hyperspectral image. However, the ensemble strategy remarkably influences the classification results, which include determining the minimum number of classifiers and assigning advisable weights associated with each classifier. In this paper, we present a novel sparse ensemble learning method with spectral-spatial knowledge for hyperspectral image classification. It considers the ensemble strategy under sparse recovery framework, where the solved non-zero coefficients reveal the importance of the selected classifier, from which a compact and effective ensemble learning system can be derived. Moreover, the spatial information is incorporated into the classification to develop a spectral-spatial joint sparse representation based ensemble learning algorithm for more accurate classification of hyperspectral images. Experimental results on several real hyperspectral images show that the proposed sparse ensemble system can achieve better performance than traditional ensemble learning methods using all classifiers, and it largely improves the efficiency in testing phase.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition - Volume 59, November 2016, Pages 42-54
Journal: Pattern Recognition - Volume 59, November 2016, Pages 42-54
نویسندگان
Erlei Zhang, Xiangrong Zhang, Licheng Jiao, Lin Li, Biao Hou,