摘要
传统主动轮廓模型很难实现精确的SAR图像河流分割。针对这一问题,本文提出了一种加权指数区域能量主动轮廓模型,以精确地提取SAR图像中的河流。该模型在Chan-Vese(CV)模型能量泛函中引入了指数区域能量,能更好地衡量分割图像和原始图像的差异程度,提高模型的分割准确性。此外,利用目标区域和背景区域内像素灰度的最大绝对差取代模型中常值区域能量权重,自适应地调节目标区域和背景区域的能量比重,加速曲线运动到目标区域的边缘,获得更高的分割效率。针对实际河流SAR图像进行了分割试验,结果表明:与传统主动轮廓模型相比,本文提出的模型能更快速、精确地分割SAR图像中的河流,在分割结果和分割效率两方面具有优势。
The traditional active contour models can hardly achieve the accurate river segmentation of SAR images.To solve this problem,a novel active contour model with weighted exponential region energy is proposed,which can extract rivers in SAR images accurately.The exponential region energy is incorporated into the energy functional of the Chan-Vese model,which can measure the difference between the segmented image and the original image,resulting in the improvement of segmentation accuracy of the model.In addition,the maximum absolute differences of the pixel grayscale values inside the object and background regions are utilized to replace the original constant region energy weights,which can adaptively adjust the ratios of the object and background region energies and accelerate the motion of the curve towards the boundaries of the object region,resulting in the higher segmentation efficiency.The experiments are performed on real SAR images of rivers and results demonstrate that compared with the traditional active contour models,the proposed model can segment rivers in SAR images more rapidly and accurately and has some advantages in terms of both segmentation performance and segmentation efficiency.
出处
《测绘学报》
EI
CSCD
北大核心
2017年第9期1174-1181,共8页
Acta Geodaetica et Cartographica Sinica
基金
国家自然科学基金(61573183)
水利部黄河泥沙重点实验室开放基金(2014006)
港口航道泥沙工程交通行业重点实验室开放基金
城市水资源与水环境国家重点实验室开放基金(LYPK201304)~~
关键词
SAR图像
河流分割
主动轮廓模型
指数区域能量
最大绝对差
SAR image
river segmentation
active contour model
exponential region energy
maximum absolute difference
