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针对逐像元处理的高光谱图像实时线性约束最小方差(LCMV)检测与分类算法计算量大、运行速度慢的问题,在LCMV检测与分类算法的基础上,提出了两种逐行的实时LCMV目标检测与分类算法。首先对LCMV算法进行了因果化,提出了逐行处理的实时因果LCMV(CR-LCMV)检测与分类算法,再利用Woodbury引理,推导出了逐行处理的实时递归因果LCMV(RCR-LCMV)检测与分类算法。实验结果表明:与LCMV检测与分类算法相比,两种新型实时算法均能在不影响检测精度的情况下实时地检测目标与对目标进行分类,且所需的数据存储空间大大降低;与逐像元处理的实时LCMV算法相比,两种新型实时算法可获得几乎与之相同的检测精度,计算复杂度大大降低,实时处理能力更强,算法在运行时间上具有明显的优越性。
According to LCMV detection and classification algorithm, two line-by-line real-time LCMV (real-time linear constrained least variance (LCMV) detection and classification algorithm based on pixel-by- Target Detection and Classification Algorithm. First of all, the LCMV algorithm has been made to be causal, a real-time causal LCMV (CR-LCMV) detection and classification algorithm is proposed, and then a Woodward Lemma is used to process the real-time recursive causal LCMV Detection and classification algorithm. The experimental results show that compared with the LCMV detection and classification algorithms, both of the new real-time algorithms can detect targets and classify targets in real time without affecting the detection accuracy, and the required data storage space is greatly reduced. Compared with the real-time LCMV algorithm of pixel-based processing, the two new real-time algorithms can obtain almost the same detection accuracy, greatly reducing the computational complexity, real-time processing capability and the algorithm has obvious superiority in running time.