.png){kind=icon}
返回
Background Representation Learning With Structural Constraint for Hyperspectral Anomaly Detection
.png){kind=icon}
PRE
AI
DOI：10.1109/LGRS.2021.3073176.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Hyperspectral anomaly detection is a popular topic in remote sensing image intelligent interpretation. To detect anomaly, many methods for background representation have been proposed. However, the prior information of background and anomaly is not fully explored in these methods. To tackle this issue, we combine low-rank dictionary learning (LRDL) with total variation (TV) constraint for hyperspectral anomaly detection. To be specific, the LRDL is introduced for background representation to explore the low-rank priori of background. Considering the smooth structural characteristic of background in spatial, we introduce the TV constraint on coefficients matrix for better background representation learning. Then the residual part is used to discriminate anomaly. The experiments on three real data sets demonstrate the effectiveness of the proposed method compared with state-of-the-art algorithms in hyperspectral anomaly detection.
Keyword:
TV
Hyperspectral imaging
Anomaly detection
Dictionaries
Complexity theory
Sparse matrices
Image reconstruction
Hyperspectral anomaly detection
low-rank dictionary learning
total variation constraint
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
16.4
论文数:
1.0W
被引数:
5.1K
机构
引用论文
Anomaly Detection in Hyperspectral Images Based on Low-Rank and Sparse Representation基于低秩稀疏表示的高光谱图像异常检测
.png){kind=icon}
PREAI
分享收藏Hyperspectral Image Classification Using Dictionary-Based Sparse Representation基于字典稀疏表示的高光谱图像分类
PREAI
分享收藏Analysis of the Evolution Game of Stakeholders’ Behaviour in the Operation Stage of Green Buildings
OAAI
OAAI分享收藏Hyperspectral Unmixing Using Sparsity-Constrained Deep Nonnegative Matrix Factorization With Total Variation基于全变分稀疏约束深度非负矩阵分解的高光谱解混
PREAI
分享收藏
查看更多内容