计算机科学与探索 ›› 2022, Vol. 16 ›› Issue (2): 448-457.DOI: 10.3778/j.issn.1673-9418.2009009
詹天明1,2, 宋博1,+(), 孙乐3, 万鸣华1, 杨国为1
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。基金资助:
ZHAN Tianming1,2, SONG Bo1,+(), SUN Le3, WAN Minghua1, YANG Guowei1
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.Supported by:
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。
中图分类号:
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.
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 |
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 |
方法 | 指标 | 数据集 | |||
---|---|---|---|---|---|
Farmland | UASFC1 | UASFC2 | |||
SVM[ | 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[ | 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[ | 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[ | 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[ | 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 |
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 | |||
---|---|---|---|---|---|
Farmland | UASFC1 | UASFC2 | |||
SVM[ | 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[ | 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[ | 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[ | 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[ | 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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