全变差稀疏约束深度非负矩阵分解高光谱遥感影像解混方法

doi:10.16180/j.cnki.issn1007-7820.2023.02.008

Abstract

Abstract:

The traditional nonnegative matrix factorization methods are based on single-layer model. The deep nonnegative matrix factorization methods are based on the mathematical theory, ignoring the actual spectral mixing process of materials. In this regard, this study starts from the physical process of spectral mixing, integrates non-negative matrix decomposition and deep learning, and reversely models the spectral mixing process. In addition, considering the sparsity and spatial smoothness of abundance, a fully-variation sparse constrained deep non-negative matrix factorization model for endmember matrix-oriented unmixing of hyperspectral remote sensing images is established. Through simulation experiments and real experiments, the proposed method is compared with five unmixing methods. The results show that compared with the abundance-oriented deep nonnegative matrix factorization algorithm, the average spectral angular distance and root mean square error of the proposed method are reduced, the best unmixing results are obtained.

Key words: hyperspectral remote sensing, hyperspectral unmixing, linear spectral unmixing, nonnegative matrix factorization, deep learning, deep nonnegative matrix factorization, sparsity constraint, total variation constraint

CLC Number:

TP751

ZHAO Wenjun,ZHAI Han,ZHANG Hongyan. Total Variation and Sparsity Regularized Deep Nonnegative Matrix Factorization for Hyperspectral Unmixing[J].Electronic Science and Technology, 2023, 36(2): 53-60.

Figures/Tables 15

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

Figure 6.

Table 1.

Figure 7.

Figure 8.

Figure 9.

Table 2.

Figure 10.

Figure 11.

Figure 12.

Figure.13

References 21

[1]	Zhai H, Zhang H Y, Zhang L P, et al. Sparsity-based clustering for large hyperspectral remote sensing images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020(11):1-15.
[2]	童庆禧, 张兵, 郑兰芬. 高光谱遥感:原理、技术与应用[M]. 北京: 高等教育出版社, 2006.
	Tong Qingxi, Zhang Bing, Zheng Lanfen. Hyperspectral remote sensing[M]. Beijing: Higher Education Press, 2006.
[3]	Keshava N, Mustard J F. Spectral unmixing[J]. IEEE Signal Processing Magazine, 2002, 19(1):44-57. doi: 10.1109/79.974727
[4]	Zhai H, Zhang H, Zhang L, et al. Laplacian-regularized low-rank subspace clustering for hyperspectral image band selection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 57(3):1723-1740. doi: 10.1109/TGRS.2018.2868796
[5]	Zhai H, Zhang H, Pingxiang L I, et al. Hyperspectral image clustering: Current achievements and future lines[J]. IEEE Geoscience and Remote Sensing Magazine, 2021, 17(7)1243-1247.
[6]	刘璐, 张洪艳, 张良培. 基于光谱加权低秩矩阵分解的高光谱影像去噪方法[J]. 电子科技, 2020, 33(5):21-27.
	Liu Lu, Zhang Hongyan, Zhang Liangpei. Hyperspectral image denoising via spectral weighted low-rank matrix approximation[J]. Electronic Science and Technology, 2020, 33(5):21-27.
[7]	魏一苇, 黄世奇. 高光谱混合像元分解技术研究综述[C]. 武汉: 第九届国家安全地球物理专题研讨会, 2013.
	Wei Yiwei, Huang Shiqi. Overview of the technique for hyperspectral unmixing[C]. Wuhan: The Ninth National Security Geophysics Symposium, 2013.
[8]	Jia S, Qian Y. Constrained nonnegative matrix factorization for hyperspectral unmixing[J]. IEEE Transactions on Geoscience and Remote Sensing, 2008, 47(1):161-173. doi: 10.1109/TGRS.2008.2002882
[9]	Lu X, Wu H, Yuan Y, et al. Manifold regularized sparse NMF for hyperspectral unmixing[J]. IEEE Transactions on Geoscience and Remote Sensing, 2012, 51(5):2815-2826. doi: 10.1109/TGRS.2012.2213825
[10]	Feng X R, Li H C, Li J, et al. Hyperspectral unmixing using sparsity-constrained deep nonnegative matrix factorization with total variation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(10):6245-6257. doi: 10.1109/TGRS.2018.2834567
[11]	Tong L, Zhou J, Qian B, et al. Adaptive graph regularized multilayer nonnegative matrix factorization for hyperspectral unmixing[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020(13):434-447.
[12]	Lee D D, Seung H S. Learning the parts of objects by non-negative matrix factorization[J]. Nature, 1999, 401(6755):788-791. doi: 10.1038/44565
[13]	Qian Y, Jia S, Zhou J, et al. Hyperspectral unmixing via L1/2 sparsity-constrained nonnegative matrix factorization[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(11):4282-4297. doi: 10.1109/TGRS.2011.2144605
[14]	He W, Zhang H, Zhang L. Total variation regularized reweighted sparse nonnegative matrix factorization for hyperspectral unmixing[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(7):3909-3921. doi: 10.1109/TGRS.2017.2683719
[15]	Zhai H, Zhang H, Zhang L, et al. Total variation regularized collaborative representation clustering with a locally adaptive dictionary for hyperspectral imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 57(1):166-180. doi: 10.1109/TGRS.2018.2852708
[16]	Zhai H, Zhang H, Zhang L, et al. Nonlocal means regularized sketched reweighted sparse and low-rank subspace clustering for large hyperspectral images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 59(5):4164-4178. doi: 10.1109/TGRS.2020.3023418
[17]	Rudin L I, Osher S, Fatemi E. Nonlinear total variation based noise removal algorithms[J]. Physica D: Nonlinear Phenomena, 1992, 60(1-4):259-268. doi: 10.1016/0167-2789(92)90242-F
[18]	Nascimento J M P, Dias J M B. Vertex component analysis: A fast algorithm to unmix hyperspectral data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(4):898-910. doi: 10.1109/TGRS.2005.844293
[19]	Cichocki A, Zdunek R. Multilayer nonnegative matrix factorisation[J]. Electronics Letters, 2006, 42(16):947. doi: 10.1049/el:20060983
[20]	Boardman J W, Kruse F A, Green R O. Mapping target signatures via partial unmixing of AVIRIS data[J]. California Institute of Technology, 1995(3):1-4.
[21]	Zhu F, Wang Y, Fan B, et al. Spectral unmixing via data-guided sparsity[J]. IEEE Transactions on Image Processing, 2014, 23(12):5412-5427. pmid: 25330488

算法	平均光谱角距离值	均方根误差
VCA-FCLS	0.030 2	0.033 0
L₁_/₂-NMF	0.025 5	0.039 4
GNMF	0.024 4	0.032 7
MLNMF	0.059 6	0.038 0
SDNMF-TV	0.027 2	0.034 7
TVSDNMF	0.019 4	0.027 9

算法	平均光谱角距离值	均方根误差
VCA-FCLS	0.066 7	0.262 5
L_1/2-NMF	0.062 6	0.279 9
GNMF	0.065 4	0.264 3
MLNMF	0.061 9	0.267 4
SDNMF-TV	0.058 2	0.254 6
TVSDNMF	0.044 3	0.240 2

Total Variation and Sparsity Regularized Deep Nonnegative Matrix Factorization for Hyperspectral Unmixing

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 15

References 21

Related Articles 15

Metrics

Comments

Recommended 10

[1]	YU Qiongfang,NIU Dongyang. Mixed Prediction of Mine Pressure Time and Space Based on LSTM Network [J]. Electronic Science and Technology, 2023, 36(2): 67-72.
[2]	ZUO Bin,LI Feifei. An Effective Segmentation Method for COVID-19 CT Image Based on Attention Mechanism and Inf-Net [J]. Electronic Science and Technology, 2023, 36(2): 22-28.
[3]	WU Tong,YU Lianzhi. The Recommendation Algorithm of Extreme Deep Factorization Machine Merged with Attention Network [J]. Electronic Science and Technology, 2023, 36(1): 38-43.
[4]	BI Jiazhen,SHEN Tuo,ZHANG Xuanxiong. A Research on Distance Measurement Between Trains in Rail Transit Based on Machine Vision [J]. Electronic Science and Technology, 2022, 35(9): 37-43.
[5]	ZHANG Qiaomu,ZHONG Qianwen,SUN Ming,LUO Wencheng,CHAI Xiaodong. Research on Dynamic Monitoring Method of Pantograph-Net Contact Position in Complex Environment [J]. Electronic Science and Technology, 2022, 35(8): 66-72.
[6]	ZHAO Xuan,ZHOU Fan,YU Hancheng. Improved YOLOv3 Model Based on New Feature Extraction and Fusion Module [J]. Electronic Science and Technology, 2022, 35(7): 40-45.
[7]	Peng CHEN,Zilong LIU. Arrhythmia Recognition Based on GAN-CNN [J]. Electronic Science and Technology, 2022, 35(3): 45-50.
[8]	DENG Lei,SUN Peiyang. Research on Network Public Opinion Monitoring System Based on Deep Learning [J]. Electronic Science and Technology, 2022, 35(12): 97-102.
[9]	YU Runrun,JIANG Xiaoyan,ZHU Kaiying,JIANG Guanghao. Real-Time Image Semantic Segmentation Based on Contextual Attention Mechanism [J]. Electronic Science and Technology, 2022, 35(12): 57-63.
[10]	ZHANG Ye,BAO Liang. Research Progress of Service Composition Based on Machine Learning [J]. Electronic Science and Technology, 2022, 35(11): 58-63.
[11]	ZHANG Wei,LIU Na,JIANG Yang,LI Qingdu. Garbage Detection and Classification Based on YOLO Neural Network [J]. Electronic Science and Technology, 2022, 35(10): 45-50.
[12]	CHENG Xiaoya,ZHANG Lei. Research on Occluded Face Recognition Method Based on Deep Learning [J]. Electronic Science and Technology, 2022, 35(1): 35-39.
[13]	ZHAO Chong,CHI Mengmeng,CHU Cong,ZHANG Peng. Research on Motion Simulation and Visual Recognition Algorithm of Guide Dog Walking Mechanism [J]. Electronic Science and Technology, 2021, 34(9): 66-72.
[14]	MA Lixin,DOU Chenfei,SONG Chencan,YANG Tianxiao. Insulator Nondestructive Testing Based on Feature Fusion CNN [J]. Electronic Science and Technology, 2021, 34(7): 26-30.
[15]	XUE Yongjie,JU Zhiyong. Fish Recognition Algorithm Based on Improved AlexNet [J]. Electronic Science and Technology, 2021, 34(4): 12-17.