摘要
遥感图像背景信息复杂,利用传统形态学进行处理时,固定结构元素的使用容易改变道路的位置和形状,影响图像分割的准确性。为此,提出了一种基于自适应形态学的遥感图像道路提取方法。首先利用非线性结构张量构造自适应椭圆结构元素并定义相应的自适应形态学运算,并根据道路特征构造形态学高低帽变换以增强道路目标;接着通过最大类间方差法实现道路的初步提取;然后设置形状参数识别图像中的目标是否为道路区域;最后通过自适应形态学滤波法去除仍与道路粘连的非道路目标,提取出独立的道路网络。实验结果表明,所提方法能够从背景信息复杂的遥感图像中完整地提取出道路,且提取精度较高。
Because the background information of a remote sensing image is complex,traditional morphology makes it easy to change the position and shape of the road when using fixed structural elements to process the image,which affects the accuracy of image segmentation.Therefore,an adapted morphology-based method of road extraction was proposed.First,the nonlinear structural tensor was used to construct adaptive elliptic structure elements and corresponding adaptive morphological operations were created.A morphological top-to-bottom hat transformation was constructed based on road features to enhance road targets.Further,the road was extracted using the maximum interclass variance method.The shape parameters were then set to identify the targets in the image that were either in a road area or not.Finally,the adaptive morphological filtering method was used to remove the non-road targets that were still attached to the road,and the independent road network was extracted.The experimental results show that this method can completely extract the road from the remote sensing images with complex background information and higher extraction accuracy.
作者
房玉品
王小鹏
李新娜
Fang Yupin;Wang Xiaopeng;Li Xinna(College of Electronics and Information Engineering,Lanzhou Jiaotong University,Lanzhou 730070,Gansu,China)
出处
《激光与光电子学进展》
CSCD
北大核心
2022年第16期125-132,共8页
Laser & Optoelectronics Progress
基金
国家自然科学基金(61761027)。
关键词
图像处理
自适应形态学
椭圆结构元素
高低帽变换
最大类间方差法
道路提取
image processing
adaptive morphology
elliptical structural element
top-hat and bottom-hat transformation
maximum interclass variance method
road extraction
