基于凸锥分析的低概率检测方法研究

Research of Low Probability Detection Based on Convex Cone Analysis

低概率检测 (LPD)方法利用图像互相关矩阵的特征向量 ,在已知目标先验信息的情况下检测图像中小概率目标 .但是 ,由于噪声的影响以及特征向量之间正交约束性 ,使其检测效果不理想 .本文提出了利用凸锥分析(CCA)来改善LPD的方法 ;它避免由特征向量的正交性约束导致的虚警概率较高的不良结果 ,同时消除图像中的条带噪声的影响 .最后 ,结合OMIS数据分析了这种方法检测小目标的效果 .

Low probability detection (LPD) is an approach for hyperspectral imagery analysis;it use the eigenvectors of the imagery's correlation matrix in detecting small targets.Unfortunately,bacause of noise and the orthogonality constraints among the eigenvectors,the results of detection are non ideal.In this paper,we use the method of Convex Cone Analysis (CCA) to improve the detectability of LPD and to eliminate the stripe noise.The experimental results are given by applying the method to the data from Operative Modulor Imaging Spectrometer (OMIS) system.