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作 者:阿茹罕 原晓斌[2] 慕晓冬 王静怡 A Ruhan;YUAN Xiaobin;MU Xiaodong;WANG Jingyi(College of Operational Support,Rocket Force University of Engineering,Xi'an 710025,China;Xi'an Institute of Optics and Precision Mechanics,Chinese Academy of Sciences 710119,China;School of Computer Science,Xi'an Shiyou University,Xi'an 710065,China)
机构地区:[1]火箭军工程大学作战保障学院,西安710025 [2]中国科学院西安光学精密机械研究所,西安710119 [3]西安石油大学计算机学院,西安710065
出 处:《光子学报》2021年第9期289-299,共11页Acta Photonica Sinica
基 金:国家自然科学基金(Nos.41301480,62002286)。
摘 要:为进一步提高高光谱异常目标检测的速度与精度,提出一种基于扩展多属性剖面和改进的Reed-Xiaoli算法相结合的快速异常目标检测方法。通过数学形态学变换从高光谱图像中提取扩展多属性剖面,同时提出一种快速局部Reed-Xiaoli算法,利用矩阵求逆引理迭代更新协方差矩阵的逆,从而降低马氏距离的计算复杂度。将扩展多属性剖面与快速局部Reed-Xiaoli算法相结合,充分利用高光谱图像的光谱信息和空间信息,有效提高探测速度与精度。在3个不同的数据集上与其他经典异常目标检测方法进行比较,实验结果表明,所提算法AUC值分别为0.9967、0.9856、0.9816,运算时间分别为21.2181 s、15.1928 s、32.3379 s。该方法在检测精度和速度上都有明显的优势,具有良好的实用价值。In order to further improve the speed and accuracy of hyperspectral abnormal target detection,a fast anomaly target detection method based on extended multi-attribute profiles and improved Reed-Xiaoli is proposed.Extended multi-attribute Profiles are extracted from the original hyperspectral images by mathematical morphological transformations.Moreover,a novel fast local Reed-Xiao algorithm is also proposed.Iteratively update inverse matrix of covariance using matrix inverse lemma,thereby reducing the computational complexity of the Mahalanobis distance.The combination of extended multi-attribute profiles and fast local Reed-Xiaoli detector effectively utilizes the spectral information and spatial information of hyperspectral images,it greatly improves the detection accuracy and reduce the running time.Experimental results on three real data sets show the AUC value of the algorithm in this paper is 0.9967,0.9856 and 0.9816 respectively.The operation time is 21.2181 s,15.1928 s and 32.3379 s respectively.The proposed method has obvious advantages in detection accuracy and speed,and has good practical value.
关 键 词:高光谱图像 异常目标检测 快速局部RX 扩展多属性剖面 Reed-Xiaoli 矩阵求逆引理
分 类 号:TP394.1[自动化与计算机技术—计算机应用技术] TH691.9[自动化与计算机技术—计算机科学与技术]
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