CAJ | 학술논문

传统分类后比较法(post-classification comparison,PCC)存在分类累积误差问题,且对单幅图像分类精度要求较高,对此,根据不同时相图像的不变信息所具有的相关性,提出了一种基于两时相图像联合分类的SAR图像变化检测方法.该方法以灰度值作为输入信息,通过相似度计算可得两时相图像对应位置像素的灰度相似度,然后求解全局相似度阈值,并用于控制基于K-均值的联合分类器对两时相图像进行联合分类,最后通过类别比较获得变化检测结果.实验结果表明本文方法不但可提高单幅图像的分类精度,而且能够精确地把不同时相图像的不变地物信息划分为同一类别,减少了分类累积误差的影响,提高了变化检测性能.
전통분류후비교법(post-classification comparison,PCC)존재분류루적오차문제,차대단폭도상분류정도요구교고,대차,근거불동시상도상적불변신식소구유적상관성,제출료일충기우량시상도상연합분류적SAR도상변화검측방법.해방법이회도치작위수입신식,통과상사도계산가득량시상도상대응위치상소적회도상사도,연후구해전국상사도역치,병용우공제기우K-균치적연합분류기대량시상도상진행연합분류,최후통과유별비교획득변화검측결과.실험결과표명본문방법불단가제고단폭도상적분류정도,이차능구정학지파불동시상도상적불변지물신식화분위동일유별,감소료분류루적오차적영향,제고료변화검측성능.
Since the classical post-classification comparison(PCC) technique was affected by a significant cumulative error and high classification precision was needed for single image, a change-detection method based on joint-classification of bi-temporal SAR images was presented according to the correlation of the unchanged information in different temporal images. The proposed method took gray-levels as an input. The similarity of gray-levels relating to two pixels at the corresponding position for bi-tempord images was obtained through similarity operator. Then the global threshold value of similarity was got, which was used to control the joint-classifier based on K-means to classify the bi-temporal images. Finally, The change-detection map was produced by comparing with both classified images. Experimental results confirm that the proposed method not only improves the precision of classification for single image but also accurately classifies the unchanged geographical information in different temporal images into the same class. The proposed method reduces the influence of the cumulative error and improves the performance of change detection.