CAJ | 학술논문

由于要配准的目标存在可能的形变,震前和震后遥感图像的配准变得很困难.为了解决这个问题,提出基于稳健的投影非负矩阵分解(RPNMF)的配准方法来精确的配准形变目标.给出一种稳健的投影非负矩阵分解方法来获得震前震后形变目标的共同投影空间,利用在共同投影空间的投影来配准形变目标.为验证该算法的有效性,做了两个实验:2008年5月12日汶川地震前后的 SAR 图像的配准;唐家山堰塞湖的变化检测.与现有方法进行比较,结果表明该方法能够有效地得到形变目标的共同投影空间,并取得了很好的配准结果;同时,堰塞湖的变化检测也得到了很好的结果.
유우요배준적목표존재가능적형변,진전화진후요감도상적배준변득흔곤난.위료해결저개문제,제출기우은건적투영비부구진분해(RPNMF)적배준방법래정학적배준형변목표.급출일충은건적투영비부구진분해방법래획득진전진후형변목표적공동투영공간,이용재공동투영공간적투영래배준형변목표.위험증해산법적유효성,주료량개실험:2008년5월12일문천지진전후적 SAR 도상적배준;당가산언새호적변화검측.여현유방법진행비교,결과표명해방법능구유효지득도형변목표적공동투영공간,병취득료흔호적배준결과;동시,언새호적변화검측야득도료흔호적결과.
@@@@For pre-and post-earthquake remote-sensing images, registration is a challenging task due to the possible deformations of the objects to be registered. To overcome this problem, a registration method based on robust projective Nonnegative Matrix Factorization is proposed to precisely register the variform objects. Firstly, a Robust Projective Nonnegative Matrix Factorization(RPNMF) method is developed to capture the common projection space of the variform objects. Secondly, a registration approach is derived from the common projection space of the variform objects. Finally, two experiments are conducted to verify the effectiveness of the proposed method:one is the SAR image registration in Wenchuan earthquake on May 12, 2008, the other is change detection of Tangjiashan barrier lake. The results show that the method is very effective in capturing the common projection space of variform objects and generalizes well for registration. Meanwhile, good performance on the change detection of barrier lake is obtained.