Region-driven distance regularized level set evolution for change detection in remote sensing images
Region-driven distance regularized level set evolution for change detection in remote sensing images
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DOI：
10.1007/s11042-017-4650-9
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发表时间：
2017-04
影响因子：
3.6
通讯作者：
Yu Lei;Jiao Shi;Jiaji Wu
中科院分区：
文献类型：
--
作者：
Yu Lei;Jiao Shi;Jiaji Wu
变化检测是遥感图像解译和理解的一项基本任务。其目的是将从多时相卫星图像获取的差异图像划分为变化区域和不变区域。在现有的遥感图像变化检测方法中，水平集方法是一种很有前途的方法。然而，经典水平集方法的初始化过程复杂且耗时，限制了其在遥感图像变化检测中的实际应用。提出了一种基于改进的区域活动轮廓模型的遥感图像无监督变化检测方法。为了消除重新初始化的过程，减少重新初始化所带来的数值误差，提出了一种改进的水平集遥感图像变化检测方法。该方法在能量函数中引入距离正则化项，使能量函数保持期望的形状，并在零水平集附近保持有符号的距离轮廓。在真实的多时相遥感图像上的实验结果证明了该方法在人眼视觉感知和分割精度方面的优势。
Change detection is a fundamental task in the interpretation and understanding of remote sensing images. The aim is to partition the difference images acquired from multitemporal satellite images into changed and unchanged regions. Level set method is a promising way for remote sensing images change detection among the existed methods. Unfortunately, re-initialization, a necessary step in classical level set methods is known a complex and time-consuming process, which may limits their practical application in remote sensing images change detection. In this paper, we present an unsupervised change detection approach for remote sensing image based on an improved region-based active contour model without re-initialization. In order to eliminate the process for re-initialization and reduce the numerical errors caused by re-initialization, we describe an improving level set method for remote sensing images change detection. The proposed method introduced a distance regularization term into the energy function which could maintain a desired shape of the level set function and keep a signed distance profile near the zero level set. The experimental results on real multi-temporal remote sensing images demonstrate the advantages of our method in terms of human visual perception and segmentation accuracy.