一种基于自适应形态学中椭圆结构元素的遥感图像水体分割方法

摘要：

利用可见光和红外遥感图像统计水体区域面积是掌握水资源基本情况的有效手段， 而水体分割是统计的前提。 通常遥感图像中水体区域的边缘特征较为复杂， 在利用传统形态学图像分割方法时， 通常采用固定结构元素对图像进行形态学运算， 导致图像边缘属性易发生改变， 进而影响图像分割准确率。 为了准确分割遥感图像中的地表水体， 提出了一种利用形态学自适应椭圆结构元素的遥感图像水体分割方法。 首先利用线性结构张量估计图像特征值和特征向量， 根据该特征属性构造可自适应变化的椭圆结构元素； 然后定义相应的自适应形态学基本运算， 进而组合衍生出相应的闭运算， 消除暗细节噪声对水体的影响且不会对水体边缘过度拉伸， 因而能够更准确的保持水体边缘； 最后在此基础上， 运用灰度切片分割出水体区域。 实验结果表明， 所提出的方法具有较高的分割准确率， 平均分割误差小于1.43%。

 The use of visible and infrared remote sensing images to calculate the water area is an effective means to grasp the basic situation of water resources, and water segmentation is the premise of statistics. Generally, the edge features of the water in the remote sensing images are complex. When the traditional morphology is used for image segmentation, it is easy to change the image edge and affect the accuracy of image segmentation because the fixed structuring elements are used to perform morphological operations on the image. To segment water in the remote sensing image accurately, a remote sensing image water segmentation method based on adaptive morphological elliptical structuring elements is proposed. Firstly, the eigenvalue and eigenvector of the image are estimated by linear structure tensor, and the elliptical structuring elements are constructed by the eigenvalue and eigenvector. Then adaptive morphological operations are defined, combining the close operation to eliminate the influence of dark detail noise on water without overstretching the water edge, so that the water edge can be maintained more accurately. Finally, on this basis, the water area can be segmented by gray slice. The experimental results show that the proposed method has higher segmentation accuracy and the average segmentation error is less than 1.43%.