高光谱协同稀疏与非局部低秩张量变化检测

doi:10.3778/j.issn.1673-9418.2009009

计算机科学与探索 ›› 2022, Vol. 16 ›› Issue (2): 448-457.DOI: 10.3778/j.issn.1673-9418.2009009

高光谱协同稀疏与非局部低秩张量变化检测

詹天明¹^,², 宋博¹^,⁺(), 孙乐³, 万鸣华¹, 杨国为¹

1.南京审计大学信息工程学院,南京 211815
2.南京审计大学审计信息工程与技术协同创新中心,南京 211815
3.南京信息工程大学计算机学院,南京 210094

收稿日期:2020-09-04 修回日期:2020-11-20 出版日期:2022-02-01 发布日期:2020-12-09
通讯作者: + E-mail: mg1909003@stu.nau.edu.cn
作者简介:詹天明（1984—）,男,江苏高邮人,博士,副教授,硕士生导师,主要研究方向为高光谱图像处理、深度学习。
宋博（1997—）,男,江苏南京人,硕士研究生,主要研究方向为高光谱图像处理。
孙乐（1987—）,男,江苏洋河人,博士,副教授,硕士生导师,主要研究方向为高光谱图像处理。
万鸣华（1978—）,男,江西南昌人,博士,副教授,硕士生导师,主要研究方向为模式识别、数据处理。
杨国为（1964—）,男,江西樟树人,博士,教授,硕士生导师,主要研究方向为人工智能、大数据。
基金资助:
国家自然科学基金面上项目(61976117);国家自然科学基金面上项目(61876213);江苏省自然科学基金面上项目(BK20191409);江苏省高校自然科学研究重大项目(19KJA360001);江苏省高校自然科学研究重大项目(18KJA520005);审计信息工程与技术协同创新中心2018年度研究课题(18CICA09);南京审计大学青年教师科研培育项目(18QNPY015);江苏省研究生科研与实践创新计划项目(KYCX20_1680)

Hyperspectral Change Detection Using Collaborative Sparsity and Nonlocal Low-Rank Tensor

ZHAN Tianming¹^,², SONG Bo¹^,⁺(), SUN Le³, WAN Minghua¹, YANG Guowei¹

1. School of Information Engineering, Nanjing Audit University, Nanjing 211815, China
2. Collaborative Innovation Center of Audit Information Engineering and Technology, Nanjing Audit University, Nanjing 211815, China
3. School of Computer Science, Nanjing University of Information Science and Technology, Nanjing 210094, China

Received:2020-09-04 Revised:2020-11-20 Online:2022-02-01 Published:2020-12-09
About author:ZHAN Tianming, born in 1984, Ph.D., associate professor, M.S. supervisor. His research interests include hyperspectral image processing and deep learning.
SONG Bo, born in 1997, M.S. candidate. His research interest is hyperspectral image processing.
SUN Le, born in 1987, Ph.D., associate professor, M.S. supervisor. His research interest is hyper-spectral image processing.
WAN Minghua, born in 1978, Ph.D., associate professor, M.S. supervisor. His research interests include pattern recognition and data processing.
YANG Guowei, born in 1964, Ph.D., professor, M.S. supervisor. His research interests include artificial intelligence and big data.
Supported by:
National Natural Science Foundation of China(61976117);National Natural Science Foundation of China(61876213);Natural Science Foundation of Jiangsu Province(BK20191409);Key Projects of University Natural Science Fund of Jiangsu Province(19KJA360001);Key Projects of University Natural Science Fund of Jiangsu Province(18KJA520005);Project of Collaborative Innovation Center of Audit Information Engineering and Technology(18CICA09);Cultivation Project of Nanjing Audit University(18QNPY015);Postgraduate Research and Practice Innovation Program of Jiangsu Province(KYCX20_1680)

摘要/Abstract

摘要：

高光谱图像变化检测可提供地球表面的时间维变化信息,对城乡规划和管理至关重要。因具有较高的光谱分辨率,高光谱图像常被用于检测更精细的变化。针对高光谱变化检测的问题,提出一种基于协同稀疏与非局部低秩张量的高光谱图像变化检测方法。该方法首先求得前后时间点的高光谱差分图像,再根据差分图像中图像块的非局部分布特点,提取不同的非局部张量簇。然后基于协同稀疏正则化和低秩正则化建立协同稀疏与非局部低秩张量变化检测模型,并采用交替方向乘子法对模型求解得到表示系数。最后根据表示系数求得张量在不同类别中的投影残差,进而根据投影残差最小准则判断该张量块是否发生变化。在Farm-land数据集和Urban area in San Francisco City数据集上进行实验,实验结果表明该方法取得较好的高光谱变化检测精度。

关键词: 高光谱, 变化检测, 协同稀疏, 非局部低秩, 张量分解

Abstract:

Hyperspectral image change detection can provide timely change information on the surface of the earth, which is essential for urban and rural planning and management. Due to the higher spectral resolution, hyperspectral images are often used to detect finer changes. Aiming at the problem of change detection by using hyperspectral image, a hyperspectral change detection method based on collaborative sparsity and nonlocal low-rank tensor is proposed. This method first obtains hyperspectral differential image at different time points, and then extracts different nonlocal similar block tensor clusters according to the nonlocal distribution characteristics of the image blocks in the differential image. Then, based on collaborative sparse regularization and low-rank regularization, a change detection model using collaborative sparsity and non-local low-rank tensor is established, and the representa-tion coefficient is obtained by solving the model using the alternating direction method of multipliers. Finally, the projection residuals of the tensor in different categories are obtained according to the representation coefficients, and then the projection residual minimization criterion is judged whether the tensor has changed. Experiments on Farm-land and Urban area in San Francisco City datasets demonstrate that the proposed method can achieve much better changes detection accuracy.

Key words: hyperspectral, change detection, collaborative sparsity, non-local low-rank, tensor decomposition

中图分类号:

TP391

詹天明, 宋博, 孙乐, 万鸣华, 杨国为. 高光谱协同稀疏与非局部低秩张量变化检测[J]. 计算机科学与探索, 2022, 16(2): 448-457.

ZHAN Tianming, SONG Bo, SUN Le, WAN Minghua, YANG Guowei. Hyperspectral Change Detection Using Collaborative Sparsity and Nonlocal Low-Rank Tensor[J]. Journal of Frontiers of Computer Science and Technology, 2022, 16(2): 448-457.

图/表 11

图1 本文方法总体流程图

Fig.1 General flow chart of proposed method

图2 Farmland数据集

Fig.2 Farmland dataset

图3 Urban area in San Francisco City第一组数据集

Fig.3 The first dataset of Urban area in San Francisco City

图4 Urban area in San Francisco City第二组数据集

Fig.4 The second dataset of Urban area in San Francisco City

图5 选取不同参数时三组数据集的平均OA值

Fig.5 Average OA values of three datasets with different parameters

图6 三组数据集取不同训练样本数量时的OA值

Fig.6 OA values of three datasets with different number of training samples

表1 不同尺度图像块在三组数据集上的OA值

Table 1 OA values of different scales image blocks on three datasets

数据集	3×3	5×5	7×7
Farmland	0.981 5	0.981 0	0.976 4
UASFC1	0.986 0	0.982 6	0.979 5
UASFC2	0.994 5	0.992 0	0.983 6

图7 不同方法在Farmland数据集上的检测结果

Fig.7 Detection results of different methods on Farmland dataset

图8 不同方法在UASFC1数据集上的检测结果

Fig.8 Detection results of different methods on UASFC1 dataset

图9 不同方法在UASFC2数据集上的检测结果

Fig.9 Detection results of different methods on UASFC2 dataset

表2 不同方法在3组数据集上的实验结果

Table 2 Experimental results of different methods on 3 datasets

方法		指标	数据集
方法		指标	Farmland	UASFC1	UASFC2
SVM^[11]		OA	0.937 6	0.942 4	0.881 0
		Kappa	0.848 3	0.706 6	0.684 8
		时间/s	504	99	16
CNN^[26]		OA	0.918 5	0.913 9	0.890 2
		Kappa	0.794 9	0.558 5	0.669 9
		时间/s	931	449	137
GETNET^[12]		OA	0.975 3	0.949 9	0.943 0
		Kappa	0.939 4	0.747 2	0.824 9
		时间/s	515	268	82
NLLRSU^[27]		OA	0.955 5	0.941 5	0.951 5
		Kappa	0.896 0	0.710 5	0.797 0
		时间/s	3 517	1 439	409
GTR^[19]		OA	0.980 5	0.969 2	0.963 0
		Kappa	0.953 2	0.845 6	0.831 0
		时间/s	62	46	15
本文方法	CST	OA	0.971 2	0.972 5	0.981 9
		Kappa	0.931 5	0.844 7	0.919 1
		时间/s	1 628	634	203
	CSNLRT	OA	0.981 5	0.986 0	0.994 5
		Kappa	0.959 0	0.939 0	0.978 4
		时间/s	3 426	1 328	394

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高光谱协同稀疏与非局部低秩张量变化检测

Hyperspectral Change Detection Using Collaborative Sparsity and Nonlocal Low-Rank Tensor

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