Hyperspectral Anomaly Detection With Tensor Average Rank and Piecewise Smoothness Constraints

高光谱成像 异常检测 像素 模式识别(心理学) 张量(固有定义) 奇异值分解 人工智能 分段 异常(物理) 计算机科学 矩阵范数 数学 数据立方体 秩(图论) 数据挖掘 物理 特征向量 数学分析 组合数学 量子力学 凝聚态物理 纯数学
作者
Siyu Sun,Jun Liu,Xun Chen,Wei Li,Hongbin Li
出处
期刊:IEEE transactions on neural networks and learning systems [Institute of Electrical and Electronics Engineers]
卷期号:34 (11): 8679-8692 被引量:12
标识
DOI:10.1109/tnnls.2022.3152252
摘要

Anomaly detection in hyperspectral images (HSIs) has attracted considerable interest in the remote-sensing domain, which aims to identify pixels with different spectral and spatial features from their surroundings. Most of the existing anomaly detection methods convert the 3-D data cube to a 2-D matrix composed of independent spectral vectors, which destroys the intrinsic spatial correlation between the pixels and their surrounding pixels, thus leading to considerable degradation in detection performance. In this article, we develop a tensor-based anomaly detection algorithm that can effectively preserve the spatial-spectral information of the original data. We first separate the 3-D HSI data into a background tensor and an anomaly tensor. Then the tensor nuclear norm based on the tensor singular value decomposition (SVD) is exploited to characterize the global low rank existing in both the spectral and spatial directions of the background tensor. In addition, the total variation (TV) regularization is incorporated due to the piecewise smoothness. For the anomaly component, the l2.1 norm is exploited to promote the group sparsity of anomalous pixels. In order to improve the ability of the algorithm to distinguish the anomaly from the background, we design a robust background dictionary. We first split the HSI data into local clusters by leveraging their spectral similarity and spatial distance. Then we develop a simple but effective way based on the SVD to select representative pixels as atoms. The constructed background dictionary can effectively represent the background materials and eliminate anomalies. Experimental results obtained using several real hyperspectral datasets demonstrate the superiority of the proposed method compared with some state-of-the-art anomaly detection algorithms.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
可乐完成签到,获得积分10
4秒前
wdy111举报徐per爱豆求助涉嫌违规
4秒前
5秒前
6秒前
6秒前
漂亮白枫完成签到,获得积分10
7秒前
情怀应助why359采纳,获得10
7秒前
CodeCraft应助wsj采纳,获得10
7秒前
7秒前
领导范儿应助DoctorDiDi采纳,获得10
7秒前
LaTeXer应助勤恳白山采纳,获得80
7秒前
10秒前
小爪冰凉发布了新的文献求助30
10秒前
11秒前
漂亮白枫发布了新的文献求助10
11秒前
11秒前
12秒前
12秒前
优雅灵波发布了新的文献求助10
13秒前
kong完成签到,获得积分10
14秒前
14秒前
JJ发布了新的文献求助10
14秒前
幸福大白发布了新的文献求助10
15秒前
16秒前
16秒前
123完成签到,获得积分10
16秒前
Qing完成签到,获得积分10
17秒前
小二郎应助搞笑5次采纳,获得10
17秒前
ZONG发布了新的文献求助20
19秒前
yyyyyyy发布了新的文献求助10
20秒前
勤奋幻柏发布了新的文献求助10
20秒前
why359发布了新的文献求助10
21秒前
21秒前
22秒前
23秒前
hahah完成签到,获得积分10
25秒前
伶俐绿柏发布了新的文献求助10
27秒前
狸宝的小果子完成签到 ,获得积分10
27秒前
汉堡包应助wzc采纳,获得10
27秒前
深情安青应助刀锋采纳,获得10
28秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
‘Unruly’ Children: Historical Fieldnotes and Learning Morality in a Taiwan Village (New Departures in Anthropology) 400
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3989450
求助须知:如何正确求助?哪些是违规求助? 3531621
关于积分的说明 11254315
捐赠科研通 3270207
什么是DOI,文献DOI怎么找? 1804928
邀请新用户注册赠送积分活动 882105
科研通“疑难数据库(出版商)”最低求助积分说明 809176