基于多尺度分解的超光谱图像异常检测  

Multiscale decomposition-based anomaly detection for hyperspectral images

作　　者：秦翰林[1] 姚柯柯[1] 程茂林[1] 周慧鑫[1] 张翔 赖睿[3] 

机构地区：[1]西安电子科技大学技术物理学院,西安710071 [2]中国兵器工业203研究所,西安710031 [3]西安电子科技大学微电子学院,西安710071

出　　处：《强激光与粒子束》2012年第2期327-330,共4页High Power Laser and Particle Beams

基　　金：中央高校基本科研业务费专项资金项目(72105623;72104810);国家自然科学基金项目(60902080)

摘　　要：提出了一种基于多尺度分解的超光谱图像异常检测算法。在目标和背景均未知的前提下,利用光谱和空间两种信息完成对异常目标信号的定位,从而实现超光谱遥感数据中异常目标检测。首先利用非下采样塔式变换对超光谱图像进行分解,将其划分为不同尺度子块;然后依据超光谱图像同一波段不同尺度空间内相邻系数的相关性,采用不同波段各个尺度空间的反锐化掩模方法优化背景数据分布,从而抑制异常数据对背景的干扰;最后利用设计的核RX算子得到异常目标检测结果。为验证方法的有效性,利用真实和模拟的AVIRIS数据进行了实验,并与经典RX算法相比较,实验结果表明,基于非下采样塔式分解的异常检测方法具有更好的检测性能和较低的虚警。An anomaly detection algorithm for hyperspectral images based on multiscale decomposition is spatial and spectral information is used to locate and detect targets under the background. Firstly, the hyperspectral images are decomposed into a condition of no p f different scaled knowledge proposed. Both about target and sub bands using nonsubsampled pyramid decomposition. Then using the correlation of neighborhood coefficient of different scaled space in a hyperspectral band, the background data is optimally predicted by reducing the anomalous data using unsharped masking filter in different scale of each band and finally the anomaly targets can be detected by using designed kernel RX operator in the feature space. Numerical experi- ments have been conducted on real and synthesized AVIRIS data to validate the effectiveness of the proposed algorithm. Compared with classical RX algorithm, the proposed algorithm has better detection performance and lower false alarm probability.

关 键 词：超光谱图像 异常检测 非下采样塔式分解 核RX算法 

分 类 号：TP751.1[自动化与计算机技术—检测技术与自动化装置]