کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6951885 | 1451707 | 2018 | 11 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Dictionary-aided hyperspectral unmixing based on constrained l2,q-l2,p optimization
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موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
پردازش سیگنال
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چکیده انگلیسی
Dictionary-aided unmixing has been introduced as a semi-supervised unmixing method, under the assumption that the observed mixed pixel of a hyperspectral image can be expressed in the form of different linear combinations of a few spectral signatures from an available spectral library. Sparse-regression-based unmixing methods have been recently proposed to solve this problem. Mostly, lp-norm minimization is a closer surrogate to the l0-norm minimization and can be solved more efficiently than l1-norm minimization. In this paper, we model the hyperspectral unmixing as a constrained l2,q-l2,p optimization problem. To effectively solve the induced optimization problems for any q (1â¤qâ¤2) and p (0
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Digital Signal Processing - Volume 73, February 2018, Pages 117-127
Journal: Digital Signal Processing - Volume 73, February 2018, Pages 117-127
نویسندگان
Fanqiang Kong, Chending Bian, Yunsong Li, Keyan Wang,