本文選題：光譜學(xué) + 光譜重構(gòu)��； 參考：《光學(xué)學(xué)報》2016年09期

【摘要】：針對常用的迭代追蹤類算法難以保證低采樣下光譜重構(gòu)的成功率與精度的問題,提出了一種在低采樣下光譜重構(gòu)中字典原子選取的優(yōu)化方法。利用AVIRIS和ROSIS高光譜數(shù)據(jù)構(gòu)建光譜稀疏字典并進(jìn)行壓縮感知光譜重構(gòu)實驗,分別從光譜重構(gòu)精度、稀疏成分提取能力、光譜重構(gòu)的成功率和光譜識別的準(zhǔn)確率等不同角度進(jìn)行了分析。實驗結(jié)果表明,本文方法不僅優(yōu)于傳統(tǒng)的匹配追蹤算法,同時也優(yōu)于公認(rèn)的精度較高的FOCUSS、MSBL等其他類型的算法。
[Abstract]:Aiming at the problem that the common iterative tracing algorithms can not guarantee the success rate and precision of spectral reconstruction under low sampling, an optimization method for selecting dictionary atoms in spectral reconstruction under low sampling is proposed. Using AVIRIS and ROSIS hyperspectral data to construct spectral sparse dictionaries and to carry out experiments of compressed sensing spectral reconstruction, respectively, from spectral reconstruction accuracy, sparse component extraction ability. The success rate of spectral reconstruction and the accuracy of spectral recognition were analyzed. The experimental results show that this method is not only superior to the traditional matching tracking algorithm, but also superior to other algorithms such as FOCUSS MSBL, which has high accuracy.
【作者單位】： 中國科學(xué)院光電研究院定量遙感信息重點(diǎn)實驗室;中國科學(xué)院大學(xué);
【基金】：國家863計劃(2013AA12904) 中國科學(xué)院/國家外國專家局創(chuàng)新國際團(tuán)隊(2013AA1229)
【分類號】：TP751

【參考文獻(xiàn)】

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本文編號：1876504