Research progress on hyperspectral anomaly detection
- Vol. 28, Issue 1, Pages: 42-54(2024)
Published： 07 January 2024
DOI： 10.11834/jrs.20232405
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摘要
随着航空航天技术与遥感技术的不断发展，遥感影像在诸多领域的应用不断拓展，其中高光谱分辨率遥感影像具有“图谱合一”的特点，即该数据既包含了具有强大区分性的地物光谱信息，又包含了丰富的地物空间位置信息，因此高光谱数据具有非常大的应用潜力。高光谱异常目标检测问题，是在对目标先验信息未知的前提下，根据光谱与空间信息实现对区域中的异常目标的进行“盲”检测，因此其在资源调查、灾害救援等领域发挥了巨大的作用，是遥感领域非常重要的研究课题。本文针对高光谱遥感影像异常目标检测研究方向，首先总结阐述了目前高光谱异常目标检测问题的主要研究进展，根据算法原理的不同对现有主流算法进行了分类与总结，主要分成了基于统计学、基于数据表达、基于数据分解、基于深度学习等不同的种类的方法，并对每类方法的特点进行分析。随后通过对现有方法的调研、分析与总结，提出了数据库拓展、多源数据融合、算法实用化等高光谱异常检测研究未来发展的3个方向。
Abstract
The applications of remote sensing images in numerous fields have been increasing with the continuous development of aerospace and remote sensing technologies. HyperSpectral Image (HSI) is a common type of remote sensing image that comprises a series of two-dimensional remote sensing images as a 3D data cube. Each two-dimensional image in HSI can reveal the reflection/radiation intensity of different wavelengths of electromagnetic waves
and each pixel of HSI corresponds to the spectral curve reflecting the spectral information in different wavelengths. Therefore
the hyperspectral remote sensing images are characterized by “spatial-spectral integration
” which contains not only spectral information with strong discriminant but also rich spatial information. Therefore
the hyperspectral data have considerable application potential.
Hyperspectral anomaly detection aims to detect pixels in a scene with different characteristics from surrounding pixels and determines them as anomalous targets without any previous knowledge of the target. Hyperspectral anomaly detection is an unsupervised process that does not require any priori information regarding the target to be measured in advance; thus
this type of detection plays a crucial role in real life. For example
anomaly target detection technology can be used to search and rescue people after a disaster
quickly determine the fire point of a forest fire
and search mineral points in mineral resource exploration. Hyperspectral anomaly detection has been a popular research direction in the area of remote sensing image processing in recent years
and a numerous researchers have conducted extensive research and achieved rich research results.
However
hyperspectral anomaly detection still encounters many difficult problems. For example
the targets of the same material may exhibit various spectral characteristics due to the different imaging equipment and environment
which may interfere with the detection results and lead to the problem of “same object with different spectra.” Meanwhile
the targets of different materials may also exhibit the problem of “different objects with different spectra.” Then
most of the existing hyperspectral anomaly detection algorithms are only in the laboratory stage and with low technology maturity. Furthermore
the hyperspectral data may have numerous spectral bands that contain a considerable amount of redundant information
which increases the difficulty of data processing. Moreover
the number of publicly available hyperspectral anomaly detection datasets is insufficient and mostly old.
In this paper
the main research progress of hyperspectral anomaly detection is first summarized. The existing mainstream algorithms are then classified and summarized. These algorithms are mainly divided into five categories: statistics-based anomaly detection methods
data expression-based anomaly detection methods
data decomposition-based anomaly detection methods
deep learning-based anomaly detection methods
and other methods. Through the investigation
analysis
and summary of the existing methods
three future development directions of hyperspectral anomaly detection are proposed. (1) Database expansion: new datasets with additional images and highly sophisticated remote sensing sensors are introduced. (2) Multisource data combination: the advantages of different imaging sensors and various types of remote sensing data are maximized. (3) Algorithm practicality: the anomaly detection algorithms are relayed for application on real platforms.
关键词
遥感高光谱遥感高光谱异常检测深度学习矩阵分解
Keywords
remote sensinghyperspectral remote sensinghyperspectral anomaly detectiondeep learningmatrix factorization
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QIAN Xueming 西安交通大学 信息与通信工程学院
LU Xiaoqiang 中国科学院西安光学精密机械研究所 光谱成像技术实验室
ZHENG Nairong 复旦大学 信息科学与工程学院 电磁波信息科学教育部重点实验室
YANG Zi’an 复旦大学 信息科学与工程学院 电磁波信息科学教育部重点实验室
SHI Xianzheng 复旦大学 信息科学与工程学院 电磁波信息科学教育部重点实验室
YANG Hong 中国科学院空天信息创新研究院;西北工业大学 电子信息学院
SUN Yue 中国科学院空天信息创新研究院
WANG Feng 复旦大学 信息科学与工程学院 电磁波信息科学教育部重点实验室
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Key Laboratory for Information Science of Electromagnetic Waves (Ministry of Education), School of Information Science and Technology, Fudan University
Aerospace Information Research Institute, Chinese Academy of Sciences
School of Electronics and Information, Northwestern Polytechnical University
Information Engineering University, Institute of Geospatial Information
School of Resource and Environmental Sciences, Wuhan University
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