Hough变换在消除遥感图像随机相位相干干扰中的应用

An Application of the Hough Transform for Removing the Random-phase Jam in the Remote Sensing Image

遥感云图在气象、军事、生产生活等领域都有着广泛的应用。但是 ,由于在多通道扫描辐射计成像过程中部分子系统的影响 ,不可避免地在信号通道中耦合进一些相干干扰 ,这些干扰的存在将在一定程度上影响对遥感云图特征提取、目标识别等定量分析方法的有效性 ,因此必须将图像中的相干干扰予以消除或抑制。在对相干干扰特性进行分析并比较已有的几种消除方法的基础上 ,提出了基于二维频域分析的变换域 Hough变换算法。实验表明 ,与邻域平均、中值滤波、经典频域滤波等消除干扰算法相比 ,该算法不仅能高质量地消除图像中的随机相位干干扰 ,同时还能有效地保留原图像中的细微影纹和边缘信息 ,并在航天遥感图像的实时采集及处理系统中获得成功应用。

Remote Sensing Images have been applied extensively in the aspects of meteorology, militray and manufacture. However, Because of the influences of some subsystems, a little correlation jam slip into the channels inevitably during the imaging of the MCSR, which will damage the validities of the mensurable analysis to the remote\|sensing Images and the precise identification to the remote\|sensing ground\|objects in lager degrees. So the correlation jams must be removed or suppressed. Based on the analysis of the merits of the correlation jam as well as comparision of several removel ways, the article presents the Hough transform algorithm upon the two\|demension frenquency space analysis, its application in remote\|sensing image processing and its performances detailly. Experiental results show, compared with the methods of neighbour average, median filter and classical frenquency space filter, this algorithm is better in the removing random\|phase jam of the images and reserves richer fine textures and edge information. On the other hand, this algorithm makes it possible that the PSNRs of the processed images by this algorithm are higher than the other removal ways above and the subjective evaluations to the processed images are better too. At the same time, this algorithm has also been applied to the real\|time acquisition and processing of austronautic remote sensing system and got the successful results.