面向对象分析的非结构化背景目标高光谱探测方法研究

针对非结构化背景探测器中背景协方差矩阵估计的局限性，提出了一种基于面向对象分析的高光谱小目标探测算法。首先对图像进行自适应迭代分割处理，将其划分为许多均质对象;然后进行正态最优分布选取，利用多元正态无偏检验选取最佳对象集;最后将此数据集合作为局部背景并结合GLR基准算法进行目标探测。该算法可以使局部背景最大化的服从正态分布，有效地将背景光谱信息和目标光谱信息分离开来，同时通过最优选取过程克服了目标信息“污染”问题。为了验证算法的有效性，利用真实的OMIS数据进行仿真实验，并与非结构化背景探测器GLR和基于K-Means聚类的改进GLR算法的检测结果比较，结果表明提出的算法具有良好的探测性能和较低的虚警概率。

Hyperspectral Unstructured Background Target Detection Approach Based on Object-Oriented Analysis

In order to reduce the limitation in background statistics estimation of unstructured background detector, a small target detection algorithm based on object-oriented analysis was proposed. After segmenting the whole imagery into many fairly homogenous regions using adaptive iterative method, multivariate normality test was applied to choose several optimal object sets which obey the law of normal distribution well. Then, the selected objects would be combined with GLR to perform target detection. This method could make the local background well fit a normal distribution and effectively separate the target signal from background, and meanwhile avoid the contamination effect through the selection of optimal objects. A simulation experiment was conducted on real OMIS data to validate the effectiveness of the proposed algorithm. The detection results were compared with those detected by the unstructured background detector GLR and improved GLR which incorporated K-Means clustering. The results show that the proposed algorithm has better detection performance and lower false alarm probability than other detection algorithms.