WEN Hao-tian， WANG Xiao-peng
（School of Electronics and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China）
Abstract： 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%.
Key words： image processing; adaptive morphology; elliptical structuring elements; remote sensing images; water segmentation; gray slice
CLD number： TP273doi： 10.3969/j.issn.1674-8042.2020.03.006
References
［1］Legaz A, álvar G, Verdú M, et al. Adaptive morphological filters based on a multiple orientation vector field dependent on image local features. Journal of Computational and Applied Mathematics, 2018, 330: 965-981.
［2］Thurley M J, Hendriks C L L. Adaptive mathematical morphology-A survey of the field. Pattern Recognition Letters, 2014, 47(47): 18-28.
［3］Shi W, Zhu X F, Zhu G. Adaptive edge detection algorithm of MRI image based on morphology. Chinese Journal of Scientific Instrument, 2013, 34(2): 408-414.
［4］Li H, Dai Z Y, Zhang L H, et al. Mathematical morphology based drainage line extraction from remote sensing image. Geomatics and Information Science of Wuhan University, 2011, 36(8): 956-959.
［5］De N, Francesco G.B, Boato G. Detecting morphological filtering of binary images. IEEE Transactions on Information Forensics & Security, 2017, 12(5): 1207-1217.
［6］Xu C Y, Feng X Z, Zhao S H, et al. Morphological segmentation of IKONOS multispectral data. Journal of Remote Sensing, 2008, 12(6): 980-986.
［7］Yan L, Jiang W W. A structure feature for automatic extraction of plantation from high-resolution remote sensing imagery. Acta Geodaetica et Cartographica Sinica, 2016, 45(9): 1065-1072.
［8］Feng W Q, Zhang Y J. Object-oriented change detection for remote sensing images based on multi-scale fusion. Acta Geodaetica et Cartographica Sinica, 2015, 44(10): 1142-1151.
［9］Lian R B, Wang W X, Li J. Road extraction from high-resolution remote sensing images base on adaptive circular template and saliency map. Acta Geodaetica et Cartographica Sinica, 2018, 47(7): 950-958.
［10］Mylonas S K, Stavrakoudis D G, Theocharis J B, et al. A local search-based GeneSIS algorithm for the segmentation and classification of remote-sensing images. IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, 2016, 9(4): 1470-1492.
［11］Wang B, Wang G Y. A coastline detection method in high resolution SAR image based on the generalized gamma distribution. Acta Electronic Sinica, 2018, 46(4): 827-833.
［12］Marques R C P, Medeiros F N, Nobre J S. SAR image segmentation based on level set approach and G0A model. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(10): 2046-2057.
［13］Ayed I B, Mitiche A, Belhadj Z. Multiregion level-set partitioning of synthetic aperture radar images. IEEE Transactions on Pattern Analysis and M achine Intelligence, 2005, 27(5): 793-800.
［14］Baumgartner J, Gimenez J, Scavuzzo M, et al. A new approach to segmentation of multispectral remote sensing images based on MRF. IEEE Geoscience and Remote Sensing Letters, 2015, 12(8): 1720-1724.
［15］Meng Y B, Liu G H, Xu S J, et al. Image segmentation method using multi-resolution Markov random field model with edge-preserving. Journal of Xi’an Jiaotong University, 2019, 53(3): 56-65.
［16］Han B, Wu Y Q, Song Y. Segmentation of remote sensing images of rivers utilizing a hybrid active contour model with regional information fusion. Journal of Image and Graphics, 2017, 22(2): 212-224.
［17］Xu C, Hua F, Sui H G, et al. Automatic water segmentation method in SAR images using multi-scale level set. Geomatics and Information Science of Wuhan University, 2014, 39(1): 27-31.
［18］Cammoun L, CastaoMoraga C A, Muoz-Moreno E, et al. A review of tensors and tensor signal processing. Tensors in Image Processing & Computer Vision, 2009, 217: 1-32.
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
文昊天， 王小鹏
（兰州交通大学 电子与信息工程学院, 甘肃 兰州 730070）
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%。
关键词:图像处理； 自适应形态学； 椭圆结构元素； 遥感图像； 水体分割； 灰度切片
引用格式：WEN Hao-tian， WANG Xiao-peng. A method of remote sensing image water segmentation based on adaptive morphological elliptical structuring elements. Journal of Measurement Science and Instrumentation, 2020, 11（3）： 236-243. ［doi： 10.3969／j.issn.1674-8042.2020.03.006］
[full text view]
