线性约束与局部对比相耦合的多光谱遥感图像中目标探测

目标探测是遥感影像信息提取中的重要内容,然而,随着目标像元数目增多和相似地物的干扰,目标探测的虚警率会明显上升。将线性约束最小方差方法(LCMV)与局部对比方法(LCM)相结合,构建了一种新的多光谱遥感图像中目标探测方法(LCLCM):首先利用样本相关矩阵对目标进行半解混,然后利用图像的空间性增强目标信息、抑制背景信息,最后进行图像归一化和图像分割。以Landsat 8多光谱图像中船只提取为例进行方法验证,LCLCM的虚警率为1.07%,优于LCMV和LCM的虚警率12.39%和11.26%,表明该方法能够进行有效稳健的目标探测。

Target Detection for Multispectral Remote Sensing Imagery Coupling Linearly Constraint and Local Contrast

Target detection is one of the important content in remote sensing imagery information extraction,however,with the increase of image size and the interference of similar objects,the false alarm rate of target detection increase obviously.This paper built a multispectral remote sensing imagery target detection method (LCLCM) by combining the linearly constrained minimum variance (LCMV) with the local contrast method (LCM):first,using the correlation matrix of some targets to partial unmix image,then,adding the spatiality to enhance the target information and inhibit the background information,finally,normalizing and segmenting the image.Taking the boat in Landsat 8 multispectral imagery as the target to test this method,the false alarm rate of LCLCM is 1.07% and better than that of LCMV and LCM,which are 12.39% and 11.26%,respectively,showing that the method could detect target effectively and robustly.