测绘学报 ›› 2015, Vol. 44 ›› Issue (11): 1255-1262.doi: 10.11947/j.AGCS.2015.20140519
吴诗婳1, 吴一全1,2,3,4,5, 周建江1, 孟天亮1
收稿日期:
2014-10-09
修回日期:
2015-03-23
出版日期:
2015-11-20
发布日期:
2015-11-25
通讯作者:
吴一全,E-mail:nuaaimage@163.com
E-mail:nuaaimage@163.com
作者简介:
吴诗婳(1992-),女,硕士生,研究方向为遥感图像处理。E-mail:wshimage@163.com
基金资助:
WU Shihua1, WU Yiquan1,2,3,4,5, ZHOU Jianjiang1, MENG Tianliang1
Received:
2014-10-09
Revised:
2015-03-23
Online:
2015-11-20
Published:
2015-11-25
Supported by:
摘要: 为了进一步提高合成孔径雷达(SAR)图像中河流分割的精度和速度,提出了一种基于人工蜂群优化的倒数灰度熵多阈值选取与改进Chan-Vese(CV)模型相结合的分割方法。考虑SAR图像中河流目标和背景类内灰度的均匀性,提出了基于蜂群优化的倒数灰度熵多阈值选取方法,以此对河流图像进行粗分割;针对基本CV模型收敛速度低、对初始条件敏感的问题,利用图像边缘强度取代Dirac函数,将粗分割结果作为改进CV模型的初始条件,对河流图像进行细分割。大量试验结果表明,所提出的分割方法无须设置初始条件,运行速度快,分割精度高。
中图分类号:
吴诗婳, 吴一全, 周建江, 孟天亮. 利用倒数灰度熵和改进Chan-Vese模型进行SAR河流图像分割[J]. 测绘学报, 2015, 44(11): 1255-1262.
WU Shihua, WU Yiquan, ZHOU Jianjiang, MENG Tianliang. SAR River Image Segmentation Based on Reciprocal Gray Entropy and Improved Chan-Vese Model[J]. Acta Geodaetica et Cartographica Sinica, 2015, 44(11): 1255-1262.
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