摘要
为提高SAR影像岸线自动分割的精度和效率,针对传统二进制(影像序列生成的金字塔步长底数为a=2)多尺度C-V模型对初始条件敏感、收敛速度低的问题,提出指数型(影像序列生成的底数为a≥1)多尺度影像序列生成方法,本方法将传统多尺度影像序列的生成方式的底数2量化为a≥1的任意数,并应用筛选因素进行自动地快速识别海岸线。从海岸线分割结果和所需时间方面与已有传统二进制C-V模型算法进行对比,实验表明本文算法在保证精度的条件下单次迭代逼近海岸线的计算量上小于传统即二进制多尺度C-V模型的单次迭代计算量,总迭代次数有所减少,时间效率有所提高,提高了岸线自动分割的精度和效率。
The coastline, which defines the boundary between the sea and land, is the benchmark for the division of national territory and the exclusive economic zones of oceans, and it is crucial to the maintenance of the rights to oceans. Extracting coastline information rapidly and accurately has become a popular issue in ocean research. However, detecting the coastline from SAR images is challenging because the information from land and sea can be mixed in a complex landform, and the reflection information from the sea area is changeable. The C-V model possesses strong detection sensitivity and anti-noise capability for coastline detection. However, the method involves complex iterations and large amounts of calculation. The speed of coastline detection is low for large-sized SAR images. Reducing the number of iterations and improving testing efficiency are the focus of this study, and the accuracy of the results are guaranteed under this premise. Given that the existing binary C-V model for coastline segmentation has low convergence speed and is highly sensitive to initial conditions, a novel approach using the exponential sequence of SAR images is proposed to improve the accuracy and efficiency of automatic coastline segmentation of SAR images. In this study, the image sequence generated by the traditional method is changed to an exponential sequence based on any bottom number greater than 1. The coastline is automatically detected based on predefined features from the generated image sequence. The main research techniques adopted are as follows.（1） The exponential multi-scale technology of the traditional C-V model that reduces image size and obtains a series of images under different spatial resolutions is introduced.（2） A low-pass filter is used to polish a small-scale image sequence, which is easy to form and has a relatively smooth boundary.（3） The exponential image sequence with different scales and polished degrees splits the coastline based on the C-V model of the level set one by one. In the division of the coastline, the high-spatial-resolution image inherits the boundary extracted by using low-spatial-resolution images in the high level and refines the coastline further through the CV model. Coastline detection accuracy is ensured, and experimental results show that the computation effort of each iteration in the presented approach is less than that in the segmentation procedure based on the binary C-V model. The amount of iteration frequency is also reduced. The multi-scale method achieves image conversion from a low scale to a high scale. The low-pass filter removes small and pointless feature information, which improves the level set of the C-V model to achieve a simple iteration, significantly reduces time, and refines the boundary. The entire process ensures efficiency, high precision of edge extraction, and fast running time. Compared with the traditional binary C-V model, the presented approach is more efficient and effective in terms of computation effort and detection accuracy. Experimental results show that the presented method accelerates the acquisition of initial level set information and shortens the time of coastline extraction. The method also removes the non-coastline body and improves the detection precision of the main coastline body, thus ensuring a robust coastline detection process.
作者
胡娇静
张继贤
许君一
赵争
HU Jiaojing;ZHANG Jixian;XU Junyi;ZHAO Zheng(College of Geomatics, Shandong University of Science and Technology, Qingdao 266510, China;State Key Laboratory, Chinese Academy of Surveying ＆Mapping Geo Spatial Information Engineering, State Key Laboratory of Surveying and Mapping Geographic Information Bureau （National Geographic Situation Monitoring Research Center）, Beijing 100830, China;National Surveying Mapping Product Quality Inspection and Testing Center, Beijing 100830, China)
出处
《遥感学报》
EI
CSCD
北大核心
2018年第3期478-486,共9页
NATIONAL REMOTE SENSING BULLETIN
基金
国家基础测绘科技计划(编号:2016KJ0103)
国家重点研发计划(编号:2017YFB0503004)~~
