针对高光谱图像的非监督目标检测问题，提出了一种基于混沌粒子群优化（PSO）投影寻踪(PP)的检测方法。混沌PSO可加快PP过程，得到更精确的最佳投影方向。利用自适应波段选择方法进行高光谱图像降维。依据对异常分布敏感的偏度和峰度设计投影指标，并采用混沌PSO搜索最佳投影方向，由此可有效地将目标信息投影至低维空间。采用直方图分割的方法从投影图像中提取出目标。针对大量图像进行了实验及检测效果的定性与定量评价，并与遗传算法PP法、RX方法的检测结果作了比较。结果表明，能够更有效地检测出高光谱图像中的目标，且所需运行时间大为减少。

Aimed at the problem of unsupervised target detection in hyperspectral image, a target detection method using projection pursuit (PP) based on chaotic particle swarm optimization (PSO) is proposed. Chaotic PSO can speed up the process of PP and get more accurate optimal projection direction. Adaptive band selection is used for the dimensional reduction of hyperspectral image. Skewness and kurtosis which are susceptible to outliers are chosen to design the projection index. And chaotic PSO is applied to search for optimal projection direction. Thus the target information can be projected into low-dimensional space effectively. The target is extracted from projection images by histogram segmentation. Experiments with qualitative and quantitative evaluation are carried out for many images, and the detection results of the proposed method are compared with those of genetic algorithm PP method and RX method. The results show that the proposed method detects target in hyperspectral images more effectively and significantly reduces the running time.