基于变换域Hough变换的遥感图像相干干扰分析

摘要

Since the correlation jam has a serious influence on the feature extraction of remote sensing images, as well as on the precise identification of the remote-sensing ground-objects, the paper not only presents small- and large-visual-angle models, but also gives corresponding transform space Hough algorithm on the basis of the quantitive analysis of the amplitude and the phase. Compared with the methods of neighbour average, median filtering and classical frequency space filtering, the algorithm proposed in the paper is useful in removing the random-phase jam of the images, reserving richer fine textures and edge information and getting higher PSNRs. The algorithm has been applied successfully to the real-time aquisition and processing system for remote sensing images.%相干干扰的存在严重地影响了对遥感图像中目标的特征提取和识别的精度，降低了各种遥感定量分析方法的有效性。通过对相干干扰的幅度和相位特性的定量分析，本文提出了相干干扰的小视场模型和大视场模型，并给出了基于变换域的Hough变换算法。实践表明，与传统的邻域平均、中值滤波、经典频域滤波等消除干扰算法相比，该算法不仅高质量地消除了遥感图像中的相干干扰，同时还能有效地保留原图像中的细微影纹和边缘信息，并获得较好的峰值信噪比。该方法已在航天遥感图像的实时采集及处理系统中获得成功应用。