SAR图像河流分割的加权指数区域能量模型

doi:10.11947/j.AGCS.2017.20170134

测绘学报 ›› 2017, Vol. 46 ›› Issue (9): 1174-1181.doi: 10.11947/j.AGCS.2017.20170134

SAR图像河流分割的加权指数区域能量模型

韩斌¹, 吴一全^1,2,3,4

1. 南京航空航天大学电子信息工程学院, 江苏南京 211106;
2. 黄河水利委员会黄河水利科学研究院水利部黄河泥沙重点实验室, 河南郑州 450003;
3. 南京水利科学研究院港口航道泥沙工程交通行业重点实验室, 江苏南京 210024;
4. 哈尔滨工业大学城市水资源与水环境国家重点实验室, 黑龙江哈尔滨 150090

收稿日期:2017-03-23 修回日期:2017-07-16 出版日期:2017-09-20 发布日期:2017-10-12
通讯作者: 吴一全 E-mail:nuaaimage@163.com
作者简介:韩斌(1990-),男,博士生,研究方向为遥感图像处理。E-mail:909907566@qq.com
基金资助:
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）

Weighted Exponential Region Energy Model for River Segmentation of SAR Images

HAN Bin¹, WU Yiquan^1,2,3,4

1. College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China;
2. Key Laboratory of the Yellow River Sediment of Ministry of Water Resources, Yellow River Institute of Hydraulic Research, Yellow River Water Resources Commission, Zhengzhou 450003, China;
3. Key Laboratory of Port, Waterway and Sedimentation Engineering of the Ministry of Transport, Nanjing Hydraulic Research Institute, Nanjing 210024, China;
4. State Key Laboratory of Urban Water Resources and Environment, Harbin Institute of Technology, Harbin 150090, China

Received:2017-03-23 Revised:2017-07-16 Online:2017-09-20 Published:2017-10-12
Supported by:
The National Natural Science Foundation of China (No. 61573183);Open Foundation of the Key Laboratory of the Yellow River Sediment of Ministry of Water Resources (No. 2014006);Open Foundation of the Key Laboratory of Port, Waterway and Sedimentation Engineering of the Ministry of Transport;Open Foundation of the State Key Laboratory of Urban Water Resource and Environment (No. LYPK201304)

摘要/Abstract

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。

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

Abstract: 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.

Key words: SAR image, river segmentation, active contour model, exponential region energy, maximum absolute difference

中图分类号:

P237

韩斌, 吴一全. SAR图像河流分割的加权指数区域能量模型[J]. 测绘学报, 2017, 46(9): 1174-1181.

HAN Bin, WU Yiquan. Weighted Exponential Region Energy Model for River Segmentation of SAR Images[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(9): 1174-1181.

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SAR图像河流分割的加权指数区域能量模型

Weighted Exponential Region Energy Model for River Segmentation of SAR Images

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