在假设单一地表遥感图像灰度起伏符合马尔可夫模型的条件下,得到了理想单一地表灰度起伏符合高斯分布的结果。将这一结果应用于遥感图像的目标检测,提出了一种新的基于背景高斯化的遥感图像目标检测方法。该方法首先将遥感图像进行高斯化处理,将其作为近似理想背景,然后将原图像与高斯化背景做差得到残差图,进而对残差图进行目标检测。由于目标本身的信息远离背景高斯化模型,因此在背景消减的过程中,目标信息得到了很好的保持,比在原图上进行目标检测性能得到了很大的提高。实验结果进一步验证了算法具有很好的检测性能。

A new target detection algorithm is presented which deals with the problem of detecting target in remote sensing image. Based on the assumption that the distribution of the homogenous land surface is a Markov random field, we analyzed that the distribution of the homogenous land surface background is Gaussian. This model leads to an efficient and effective detector for discriminating man-made objects in real remote sensing imagery. First, the original image is transformed into Gaussian space as the ideal background. And then, the residual image is obtained by subtracting the ideal background from the original images. Finally, a conventional detector is applied to the residual image to complement the further target detection. Because the targets have values deviating significantly from the distribution of the background, the background can be severely decreased during the subtraction. Therefore, the new algorithm has better performance. Some experiments of real remote sensing images proved the validity of the new algorithm.