基于方差最小的高光谱目标探测算法研究

Research of Hyperspectral Target Detecti on Algorithms Based on Variance Minimum

目标探测技术是遥感理论与应用中的重要领域之一,由于高光谱遥感图像能够同时提供地物目标的辐射、几何和光谱信息,与其他多光谱遥感图像相比,能更好地进行目标识别。从信息论中的自信息概念出发,针对探测结果影像中目标突出且信息确定性强的特征,提出了基于方差最小(BVM)的目标检测算子。利用不同空间分辨率和光谱分辨率的高光谱影像数据进行实验,并与约束能量最小化(CEM)算子的应用效果进行了比较分析。实验结果表明,基于方差最小的算子具有更稳健的探测性能。

Target detection is one of the most important aspects in remote sensing theory and application.Hyperspectral image can provide radiation,geometrical and spectral information of targets simultaneously,making target detection much better than other methods.A target detection algorithm based on variance minimum（BVM） which makes use of highlighting information of detection results is presented.And two experiments on different spatial resolution and spectral resolution are conducted to compare BVM method and constrained energy minimization（CEM）.Results show the more robust performance of BVM method.