Superpixel segmentation method of high-resolution remote sensing image based on fuzzy clustering

doi:10.11947/j.AGCS.2020.20190135

Abstract

Abstract: Traditional fuzzy C-means clustering (FCM) only considers the gray features of image in image segmentation, which results in unsatisfactory segmentation results when the algorithm is applied to high-resolution remote sensing image segmentation. In order to solve this problem, a new method of superpixel segmentation method of high-resolution remote sensing image based on fuzzy C-means clustering is proposed in this paper. Firstly, watershed transform algorithm is used to generate multiple superpixels, and then the similarity of spectrum features among superpixels are compared. Finally, these superpixels are merged by a FCM method combined with spectrum features. Four sets of remote sensing images of different scenes were selected in the experiment, and the experimental results were evaluated by combining qualitative and quantitative methods. The experimental results show that the method can effectively improve accuracy of the segmentation and achieve better visual effect of the segmentation.

Key words: high spatial resolution remote sensing image, superpixel, image segmentation, watershed transformation, fuzzy clustering

CLC Number:

P237

HUANG Liang, YAO Bingxiu, CHEN Pengdi, YANG Xing, FU Bihuan. Superpixel segmentation method of high-resolution remote sensing image based on fuzzy clustering[J]. Acta Geodaetica et Cartographica Sinica, 2020, 49(5): 589-597.

References

[1] CHEHATA N, ORNY C, BOUKIR S, et al. Object-based change detection in wind storm-damaged forest using high-resolution multispectral images[J]. International Journal of Remote Sensing, 2014, 35(13): 4758-4777.
[2] ZHANG Zhiqiang, ZHANG Xinchang, SUN Ying, et al. Road centerline extraction from very-high-resolution aerial image and LiDAR data based on road connectivity[J]. Remote Sensing, 2018, 10(8): 1284.
[3] GU Haiyan, LI Haitao, YAN Li, et al. An object-based semantic classification method for high resolution remote sensing imagery using ontology[J]. Remote Sensing, 2017, 9(4): 329.
[4] 刘婧, 李培军. 结合结构和光谱特征的高分辨率影像分割方法[J]. 测绘学报, 2014, 43(5): 466-473. LIU Jing, LI Peijun. A High resolution image segmentation method by combined structural and spectral characteristics[J]. Acta Geodaetica et Cartographica Sinica, 2014, 43(5): 466-473.
[5] 周成虎, 骆剑承. 高分辨率卫星遥感影像地学计算[M]. 北京: 科学出版社, 2009: 14-15. ZHOU Chenghu, LUO Jiancheng. Geo-computation of high-resolution satellite remote sensing image[M]. Beijing: Science Press, 2009: 14-15.
[6] 董志鹏, 王密, 李德仁. 一种融合超像素与最小生成树的高分辨率遥感影像分割方法[J]. 测绘学报, 2017, 46(6): 734-742. DOI: 10.11947/j.AGCS.2017.20160514. DONG Zhipeng, WANG Mi, LI Deren. A high resolution remote sensing image segmentation method by combining superpixels with minimum spanning tree[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(6): 734-742. DOI: 10.11947/j.AGCS.2017.20160514.
[7] HUANG Xin, ZHANG Liangpei. A comparative study of spatial approaches for urban mapping using hyperspectral ROSIS images over Pavia City, northern Italy[J]. International Journal of Remote Sensing, 2009, 30(12): 3205-3221.
[8] MIAO Ziming, FU Kun, SUN Hao, et al. Automatic water-body segmentation from high-resolution satellite images via deep networks[J]. IEEE Geoscience & Remote Sensing Letters, 2018, 15(4): 602-606.
[9] ZHANG Libao, ZHANG Yingying. Airport detection and aircraft recognition based on two-layer saliency model in high spatial resolution remote-sensing images[J]. IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, 2017, 10(4): 1511-1524.
[10] REN Xiaofeng, MALIK J. Learning a classification model for segmentation[C]//Proceedings of the 9th IEEE International Conference on Computer Vision. Nice, France: IEEE, 2003: 10-17.
[11] WANG Murong, LIU Xiabi, GAO Yixuan, et al. Superpixel segmentation: a benchmark[J]. Signal Processing: Image Communication, 2017, 56: 28-39.
[12] ACHANTA R, SHAJI A, SMITH K, et al. SLIC superpixels compared to state-of-the-art superpixel methods[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2012, 34(11): 2274-2282.
[13] COMANICIU D, MEER P. Mean shift: a robust approach toward feature space analysis[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2002, 24(5): 603-619.
[14] 张建廷, 张立民. 结合光谱和纹理的高分辨率遥感图像分水岭分割[J]. 武汉大学学报(信息科学版), 2017, 42(4): 449-455, 467. ZHANG Jianting, ZHANG Limin. A watershed algorithm combining spectral and texture information for high resolution remote sensing image segmentation[J]. Geomatics and Information Science of Wuhan University, 2017, 42(4): 449-455, 467.
[15] XIANG Shiming, PAN Chunhong, NIE Feiping, et al. TurboPixel segmentation using Eigen-images[J]. IEEE Transactions on Image Processing, 2010, 19(11): 3024-3034.
[16] THEILER J P, GISLER G. Contiguity-enhanced k-means clustering algorithm for unsupervised multispectral image segmentation[C]//Proceedings Volume 3159, Algorithms, Devices, and Systems for Optical Information Processing. San Diego, CA, United States:SPIE, 1997: 108-118.
[17] FAN Jianchao, WANG Jun. A two-phase fuzzy clustering algorithm based on neurodynamic optimization with its application for PolSAR image segmentation[J]. IEEE Transactions on Fuzzy Systems, 2018, 26(1): 72-83.
[18] VERMA H, AGRAWAL R K, SHARAN A. An improved intuitionistic fuzzy C-means clustering algorithm incorporating local information for brain image segmentation[J]. Applied Soft Computing, 2016, 46: 543-557.
[19] ILIADIS L S, VANGELOUDH M, SPARTALIS S. An intelligent system employing an enhanced fuzzy C-means clustering model: application in the case of forest fires[J]. Computers & Electronics in Agriculture, 2010, 70(2): 276-284.
[20] LEI Tao, JIA Xiaohong, ZHANG Yanning, et al. Significantly fast and robust fuzzy C-means clustering algorithm based on morphological reconstruction and membership filtering[J]. IEEE Transactions on Fuzzy Systems, 2018, 26(5): 3027-3041.
[21] 陈放, 杨艳. 基于超像素和模糊聚类的医学超声图像分割算法[J]. 半导体光电, 2016, 37(1): 146-150. CHEN Fang, YANG Yan. Segmentation of medical ultrasound images based on superpixel and fuzzy clustering method[J]. Semiconductor Optoelectronics, 2016, 37(1): 146-150.
[22] LEI Tao, JIA Xiaohong, ZHANG Yanning, et al. Superpixel-based fast fuzzy C-means clustering for color image segmentation[J]. IEEE Transactions on Fuzzy Systems, 2019, 27(9): 1753-1766.
[23] VINCENT L. Morphological grayscale reconstruction in image analysis: applications and efficient algorithms[J]. IEEE Transactions on Image Processing, 1993, 2(2): 176-201.
[24] 季顺平, 魏世清. 遥感影像建筑物提取的卷积神经元网络与开源数据集方法[J]. 测绘学报, 2019, 48(4): 448-459. DOI: 10.11947/j.AGCS.2019.20180206. JI Shunping, WEI Shiqing. Building extraction via convolutional neural networks from an open remote sensing building dataset[J]. Acta Geodaetica et Cartographica Sinica, 2019, 48(4): 448-459.DOI: 10.11947/j.AGCS.2019.20180206.
[25] UNNIKRISHNAN R, PANTOFARU C, HEBERT M. Toward objective evaluation of image segmentation algorithms[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2007, 29(6): 929-944.