基于选择样本自相关阵的高光谱图像约束能量最小化算法  

Constrained Energy Minimization Algorithm Based on Selected Sample Correlation Matrix for Hyperspectral Imagery

作　　者：何元磊 王静荔[2] 李红军 高正明 尼涛 李旭渊 

机构地区：[1]陕西150信箱11分箱,陕西宝鸡721006 [2]空军工程大学理学院,西安710051

出　　处：《南昌航空大学学报（自然科学版）》2014年第4期13-19,共7页Journal of Nanchang Hangkong University(Natural Sciences)

基　　金：国家自然科学基金(41174093)

摘　　要：约束能量最小化算法是一种经典的高光谱图像目标检测算法,其在检测大目标地物时,背景自相关阵易受目标信息的干扰,从而影响算法的检测性能。针对此问题,提出一种基于选择样本自相关阵的CEM算法。通过比较高光谱图像每个样本像元与目标信号的光谱相似度,引入光谱相似性阈值,选择与目标信号光谱差异性较大的部分样本像元参与背景自相关阵的构建,使背景自相关阵更好地对背景的统计性进行描述。对AVIRIS获取的两幅真实高光谱图像进行了目标检测实验,结果表明,本算法的检测率有了明显提高,更适合于检测占据一定像元数目的大目标。Constrained Energy Minimization （CEM） is a classic target detection algorithm of hyperspectral imagery. CEM uses the background correlation matrix to describe the image ground, whereas the background correlation matrix is very sensitive to target information. In order to solve the problem a CEM algorithm based on selected sample correlation matrix is developed, which compares the similarity between each sample pixel and the target signal using a spectral similarity measure, and then selects the sample vectors most dissimilar with the target signal to estimate the background sample correlation matrix. Two real AVIRIS hyperspectral data sets were tested for target detection. The experimental results demonstrate that the proposed algorithm yields better detection performance and is more suitable for detecting targets occupying a number of pixels.

关 键 词：高光谱 目标检测 约束能量最小化 光谱相似性 

分 类 号：TP75[自动化与计算机技术—检测技术与自动化装置]