CAJ | 학술논문

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。
상간간우적존재엄중지영향료대요감도상중목표적특정제취화식별적정도，강저료각충요감정량분석방법적유효성。통과대상간간우적폭도화상위특성적정량분석，본문제출료상간간우적소시장모형화대시장모형，병급출료기우변환역적Hough변환산법。실천표명，여전통적린역평균、중치려파、경전빈역려파등소제간우산법상비，해산법불부고질량지소제료요감도상중적상간간우，동시환능유효지보류원도상중적세미영문화변연신식，병획득교호적봉치신조비。해방법이재항천요감도상적실시채집급처리계통중획득성공응용。
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.