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                  [1]崔穎,王恒,朱海峰.結構張量全變差再優化稀疏高光譜解混[J].哈爾濱工程大學學報,2020,41(7):1087-1093.[doi:10.11990/jheu.201901096]
                   CUI Ying,WANG Heng,ZHU Haifeng.Structural-tensor total-variation re-optimization sparse hyperspectral unmixing[J].hebgcdxxb,2020,41(7):1087-1093.[doi:10.11990/jheu.201901096]
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                  結構張量全變差再優化稀疏高光譜解混(/HTML)
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                  《哈爾濱工程大學學報》[ISSN:1006-6977/CN:61-1281/TN]

                  卷:
                  41
                  期數:
                  2020年7期
                  頁碼:
                  1087-1093
                  欄目:
                  出版日期:
                  2020-07-05

                  文章信息/Info

                  Title:
                  Structural-tensor total-variation re-optimization sparse hyperspectral unmixing
                  作者:
                  崔穎 王恒 朱海峰
                  哈爾濱工程大學 信息與通信工程學院, 黑龍江 哈爾濱 150001
                  Author(s):
                  CUI Ying WANG Heng ZHU Haifeng
                  College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
                  關鍵詞:
                  高光譜遙感光譜解混稀疏解混空間信息結構張量全變差重建誤差解混成功率
                  分類號:
                  TN751.1
                  DOI:
                  10.11990/jheu.201901096
                  文獻標志碼:
                  A
                  摘要:
                  為改善全變差正則化變量分離與增廣拉格朗日(SUnSAL-TV)算法求解的豐度存在過平滑與邊界模糊的現象,本文提出結構張量全變差(STV)再優化的稀疏解混算法(SUnSAL-TV-STV),用STV正則項校正SUnSAL-TV算法求解的豐度矩陣。本文在合成數據集與真實高光譜數據集上進行算法仿真AG复古花园,合成數據實驗結果表明:本文算法與其他算法相比,解混重建誤差提高0.01~0.03且具有最高的解混成功率AG复古花园,通過對真實數據解混豐度圖的觀察,本文算法較好地修復了SUnSAL-TV算法求解豐度圖的過平滑與邊界模糊現象。

                  參考文獻/References:

                  [1] BIOUCAS-DIAS J M, PLAZA A, DOBIGEON N, et al. Hyperspectral unmixing overview:geometrical, statistical, and sparse regression-based approaches[J]. IEEE journal of selected topics in applied earth observations and remote sensing, 2012, 5(2):354-379.
                  [2] IORDACHE M D, BIOUCAS-DIAS J M, PLAZA A. Sparse unmixing of hyperspectral data[J]. IEEE transactions on geoscience and remote sensing, 2011, 49(6):2014-2039.
                  [3] IORDACHE M D, BIOUCAS-DIAS J M, PLAZA A. Collaborative sparse regression for hyperspectral unmixing[J]. IEEE transactions on geoscience and remote sensing, 2014, 52(1):341-354.
                  [4] ZHANG Shaoquan, LI Jun, LIU Kai, et al. Hyperspectral unmixing based on local collaborative sparse regression[J]. IEEE geoscience and remote sensing letters, 2016, 13(5):631-635.
                  [5] TONG Lei, ZHOU Jun, LI Xue, et al. Region-based structure preserving nonnegative matrix factorization for hyperspectral unmixing[J]. IEEE journal of selected topics in applied earth observations and remote sensing, 2017, 10(4):1575-1588.
                  [6] IORDACHE M D, BIOUCAS-DIAS J M, PLAZA A. Total variation spatial regularization for sparse hyperspectral unmixing[J]. IEEE transactions on geoscience and remote sensing, 2012, 50(11):4484-4502.
                  [7] LEFKIMMIATIS S, ROUSSOS A, MARAGOS P, et al. Structure tensor total variation[J]. SIAM journal on imaging sciences, 2015, 8(2):1090-1122.
                  [8] AKHTAR N, SHAFAIT F, MIAN A. Futuristic greedy approach to sparse unmixing of hyperspectral data[J]. IEEE transactions on geoscience and remote sensing, 2015, 53(4):2157-2174.
                  [9] AKHTAR N, SHAFAIT F, MIAN A. SUnGP:a greedy sparse approximation algorithm for hyperspectral unmixing[C]//Proceedings of the 22nd International Conference on Pattern Recognition. Stockholm, Sweden:IEEE, 2014:3726-3731.
                  [10] LAI Mingjun. On sparse solutions of underdetermined linear systems[J]. Journal of concrete and applicable mathematics, 2010, 8(2):296-327.
                  [11] WU Zhaojun, WANG Qiang, JIN Jing, et al. Structure tensor total variation-regularized weighted nuclear norm minimization for hyperspectral image mixed denoising[J]. Signal processing, 2017, 131:202-219.
                  [12] LEFKIMMIATIS S, WARD J P, UNSER M. Hessian schatten-norm regularization for linear inverse problems[J]. IEEE transactions on image processing, 2013, 22(5):1873-1888.
                  [13] HE Wei, ZHANG Hongyan, ZHANG Liangpei. Total variation regularized reweighted sparse nonnegative matrix factorization for hyperspectral unmixing[J]. IEEE Transactions on geoscience and remote sensing, 2017, 55(7):3909-3921.
                  [14] HE B S, YANG H, WANG S L. Alternating direction method with self-adaptive penalty parameters for monotone variational inequalities[J]. Journal of optimization theory and applications, 2000, 106(2):337-356.
                  [15] WANG S L, LIAO L Z. Decomposition method with a variable parameter for a class of monotone variational inequality problems[J]. Journal of optimization theory and applications, 2001, 109(2):415-429.
                  [16] LI Y, ZHANG S, DENG C, et al. Reweighted local collaborative sparse regression for hyperspectral unmixing[J]. Infrared physics & technology, 2019, 97:277-286.
                  [17] BIOUCAS-DIAS J M, NASCIMENTO J M P. Hyperspectral subspace identification[J]. IEEE transactions on geoscience and remote sensing, 2008, 46(8):2435-2445.
                  [18] IORDACHE M D, BIOUCAS-DIAS J M, PLAZA A. Dictionary pruning in sparse unmixing of hyperspectral data[C]//Proceedings of the 4th Workshop on Hyperspectral Image and Signal Processing:Evolution in Remote Sensing. Shanghai, China:IEEE, 2012:1-4.
                  [19] TANG Wei, SHI Zhenwei, WU Ying. Regularized simultaneous forward-backward greedy algorithm for sparse unmixing of hyperspectral data[J]. IEEE transactions on geoscience and remote sensing, 2014, 52(9):5271-5288.
                  [20] YUAN Yuan, FENG Yachuang, LU Xiaoqiang. Projection-based NMF for hyperspectral unmixing[J]. IEEE journal of selected topics in applied earth observations and remote sensing, 2015, 8(6):2632-2643.
                  [21] LEFKIMMIATIS S, OSHER S. Nonlocal structure tensor functionals for image regularization[J]. IEEE transactions on computational imaging, 2015, 1(1):16-29.

                  相似文獻/References:

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                   ZHAO Chunhui,CUI Shiling,ZHAO Genping.An improved multi-endmember hyperspectral unmixing algorithm[J].hebgcdxxb,2015,(7):1281.[doi:10.11990/jheu.201405042]
                  [2]趙春暉,尤偉,齊濱,等.基于Hausdorff度量的高光譜異常目標檢測算法[J].哈爾濱工程大學學報,2016,37(07):979.[doi:10.11990/jheu.201506087]
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                  備注/Memo

                  備注/Memo:
                  收稿日期:2019-01-31。
                  基金項目:國家自然科學基金項目(61675051);教育部博士點基金項目(20132304110007).
                  作者簡介:崔穎,女,副教授;朱海峰,男,博士.
                  通訊作者:朱海峰,E-mail:zhuhaifeng@hrbeu.edu.cn.
                  更新日期/Last Update: 2020-08-15
                  AG复古花园

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