基于空-谱结合的稀疏高光谱异常目标检测

针对稀疏表示理论用于高光谱图像异常目标检测存 在检测精度不高的问题,在对高光谱图像的空间特性和光谱特性充分分析基 础上,提出了基于空-谱结合的 稀疏高光谱异常目标检测算法。首先利用多尺度高斯滤波对原始高光谱图像进行滤波 处理,通过滤波减少高光谱图像 含有的噪声对异常目标的影响；对滤波之后的高光谱图像进行波段子集划分,划分依据是邻 波段间的相关系数；然后利用高 光谱图像稀疏差异指数对每个子空间进行异常目标检测；最后对检测结果进行叠加,得到最 终异常目标检测结果。采用真实 的AVIRIS高光谱图像对算法进行仿真验证的结果表明,本文算法检测精度高,虚警率低, 提高了稀疏表示理论用于高光谱异常目标的检测性能。

Hyperspectral image sparsity anomaly targets detection based on spatial-spect ral combination

This algorithm is proposed for sparse representation anomaly target detection of hyperspectral image based on the spatial-spectral combination,which makes full use of t he spatial and spectral properties of hyperspectral image.Firstly,the multi-scale Gauss filter is used to filt er the original hyperspectral image,which will reduce the influence of the noise on anomaly targets.Second ly,the subspace is divided through the correlation coefficients between adjacent bands of hyperspectral images.Then,the subspaces are detected for the anoma ly objects to use the sparsity divergence index.Finally, we get the result of final target detection to superimpose subspace detection results.The simulation results show that this algorithm has high detection accuracy,and low false alarm rate,which improves the ability of hyperspectral anomaly target detection based on sparsity representation theo ry.