CAJ | 학술논문

对高分辨率遥感图像进行去噪是遥感研究中的一个重要难题。本文提出了一种新的基于稀疏表示的高分辨率遥感图像去噪算法，该算法根据加噪高分辨率遥感图像的特点利用 K-SVD 算法自适应的学习得到能高效描述遥感图像内容的字典，利用稀疏表示实现去噪，并且保留原图像的有用信息。通过对“高分一号”获取的遥感图像进行实验表明，该算法能较好地滤除遥感图像的噪声，提高了图像的峰值信噪比，该方法比其他字典学习算法及其他去噪算法具有更好的性能。
대고분변솔요감도상진행거조시요감연구중적일개중요난제。본문제출료일충신적기우희소표시적고분변솔요감도상거조산법，해산법근거가조고분변솔요감도상적특점이용 K-SVD 산법자괄응적학습득도능고효묘술요감도상내용적자전，이용희소표시실현거조，병차보류원도상적유용신식。통과대“고분일호”획취적요감도상진행실험표명，해산법능교호지려제요감도상적조성，제고료도상적봉치신조비，해방법비기타자전학습산법급기타거조산법구유경호적성능。
Denoising the high resolution remote sensing images is a difficult problem in the relative research field of remote sensing. A novel algorithm for denoising the high resolution remote sensing images is proposed based on sparse representation. A dictionary which has an efficient description of remote sensing image content is obtained based on K-SVD algorithm according to the characteristics of the added noise of high spatial resolution remote sensing images. Denoising is realized by using sparse representation, and the useful information of the image is kept. The experimental results of the remote sensing images obtained by“the first satellite of high resolution”show that the algorithm can filter out the noise in the image more effectively and improve the PSNR, and this method has better performance than other dictionary learning algorithms and other denoising algorithms.