基于椭圆结构元素的自适应形态学运算方法

传统形态学使用固定结构元素对图像进行形态学运算时,容易使图像边缘属性发生改变的。首先计算图像的线性结构张量;然后得到线性结构张量的特征值和特征向量,以此特征确定椭圆结构元素的长短半轴以及方向;最后在构造椭圆结构元素的基础上,定义相应的自适应形态学膨胀和腐蚀以及开闭运算。实验结果表明:利用椭圆结构元素的自适应形态学运算相比传统形态学运算和其他自适应形态学运算,具有较高的边缘保持特性和抗噪性能,梯度幅值相似性偏差和失真较小。

利用自适应形态学实现遥感水体图像增强

遥感水体图像内容信息复杂,为了滤除水体外噪声干扰方便后续水体区域分析,在保持水体区域边缘的同时增强对比度较低的遥感水体图像。提出了一种利用自适应椭圆结构元素的形态学遥感水体图像对数增强方法。利用线性结构张量估计图像特征值和特征向量,利用该特征建立自适应椭圆结构元素,更好地保持水体边缘;定义自适应形态学膨胀腐蚀,组合衍生出自适应形态学开闭运算;利用自适应形态学运算构造对数增强运算,达到滤除非目标区域噪声并增强遥感水体图像的目的。实验结果表明,相比传统和自适应形态学增强方法,该方法不仅能够滤除目标外噪声干扰,增强水体区域目标,而且保证水体边缘位置和形状,提高了对比度和信息熵。

A method of remote sensing image water segmentation based on adaptive morphological elliptical structuring elements

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%.

基于自适应形态学的遥感图像道路提取

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。