A High Resolution Remote Sensing Image Segmentation Method by Combining Superpixels with Minimum Spanning Tree

doi:10.11947/j.AGCS.2017.20160514

Abstract

Abstract: Image segmentation is the basic and key step of object-oriented remote sensing image analysis. Conventional image segmentation method is sensitive to image noise and hard to determine the correct segmentation scale. To solve these problems, a novel image segmentation method by combining superpixels with minimum spanning tree was proposed in this paper. First, the image is over-segmented by simple linear iterative clustering algorithm to obtain superpixels. Then, superpixels are firstly clustered by regionalization with dynamically constrained agglomerative clustering and partitioning algorithm using the initial segmentation number and the sum of squared deviations (SSD), local variance (LV), rate of LV change (ROC-LV) index of graphs corresponding to the segmentation number are obtained. So the suitable image segmentation number is determined according to the SSD, LV, ROC-LV index of graphs corresponding to segmentation number. Finally, superpixels are reclustered by regionalization with dynamically constrained agglomerative clustering and partitioning algorithm based on the suitable segmentation number. The experimental results showed that the proposed method can obtain good segmentation results.

Key words: high resolution remote sensing image, remote sensing image segmentation, superpixels, clustering, region merging

CLC Number:

P237

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.

References

[1] 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. DOI: 10.13485/j.cnki.11-2089.2014.0087. 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. DOI: 10.13485/j.cnki.11-2089.2014.0087.
[2] 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. ZHOU Chenghu, LUO Jiancheng. Geo-computing of High Resolution Satellite Remote Sensing Image[M]. Beijing: Science Press, 2009.
[3] YI Lina, ZHANG Guifeng, WU Zhaocong. A Scale-synthesis Method for High Spatial Resolution Remote Sensing Image Segmentation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2012, 50(10): 4062-4070.
[4] LI Deren, ZHANG Guifeng, WU Zhaocong, et al. An Edge Embedded Marker-based Watershed Algorithm for High Spatial Resolution Remote Sensing Image Segmentation[J]. IEEE Transactions on Image Processing, 2010, 19(10): 2781-2787.
[5] COMANICIU D, MEER P. Mean Shift: A Robust Approach toward Feature Space Analysis[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(5): 603-619.
[6] VINCENT L, SOILLE P. Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1991, 13(6): 583-598.
[7] ZHANG Xueliang, XIAO Pengfeng, FENG Xuezhi, et al. Toward Evaluating Multiscale Segmentations of High Spatial Resolution Remote Sensing Images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(7): 3694-3706.
[8] 巫兆聪, 胡忠文, 张谦, 等. 结合光谱、纹理与形状结构信息的遥感影像分割方法[J]. 测绘学报, 2013, 42(1): 44-50. WU Zhaocong, HU Zhongwen, ZHANG Qian, et al. On Combining Spectral, Textural and Shape Features for Remote Sensing Image Segmentation[J]. Acta Geodaetica et Cartographica Sinica, 2013, 42(1): 44-50.
[9] 李慧, 唐韵玮, 刘庆杰, 等. 一种改进的基于最小生成树的遥感影像多尺度分割方法[J]. 测绘学报, 2015, 44(7): 791-796. DOI: 10.11947/j.AGCS.2015.20140060. LI Hui, TANG Yunwei, LIU Qingjie, et al. An Improved Algorithm Based on Minimum Spanning Tree for Multi-scale Segmentation of Remote Sensing Imagery[J]. Acta Geodaetica et Cartographica Sinica, 2015, 44(7): 791-796. DOI: 10.11947/j.AGCS.2015.20140060.
[10] YUAN Jiangye, WANG Deliang, LI Rongxing. Remote Sensing Image Segmentation by Combining Spectral and Texture Features[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(1): 16-24.
[11] BAATZ M, SCHÄPE A. Multiresolution Segmentation: An Optimization Approach for High Quality Multi-scale Image Segmentation[J]. Journal of Photogrammetry and Remote Sensing, 2000, 58(3-4): 12-23.
[12] REN Xiaofeng, MALIK J. Learning a Classification Model for Segmentation[C]//Proceedings of 9th IEEE International Conference on Computer Vision. France: IEEE, 2003: 10-17.
[13] ACHANTA R, SHAJI A, SMITH K, et al. SLIC Superpixels Compared to State-of-the-art Superpixel Methods[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(11): 2274-2282.
[14] 王春瑶, 陈俊周, 李炜. 超像素分割算法研究综述[J]. 计算机应用研究, 2014, 31(1): 6-12. WANG Chunyao, CHEN Junzhou, LI Wei. Review on Superpixel Segmentation Algorithms[J]. Application Research of Computers, 2014, 31(1): 6-12.
[15] FELZENSZWALB P F,HUTTENLOCHER D P.Efficient Graph-based Image Segmentation[J].International Journal of Computer Vision, 2004, 59(2): 167-181.
[16] 董志鹏, 梅小明, 陈杰, 等. 简单线性迭代聚类的高分辨率遥感影像分割[J]. 遥感信息, 2016, 31(6): 83-88. DONG Zhipeng, MEI Xiaoming, CHEN Jie, et al. Image Segmentation of High Resolution Remote Sensing Imagery Based on Simple Linear Iterative Clustering[J]. Remote Sensing Information, 2016, 31(6): 83-88.
[17] GUO D. Regionalization with Dynamically Constrained Agglomerative Clustering and Partitioning (REDCAP)[J]. International Journal of Geographical Information Science, 2008, 22(7): 801-823.
[18] 明冬萍, 王群, 杨建宇. 遥感影像空间尺度特性与最佳空间分辨率选择[J]. 遥感学报, 2008, 12(4): 529-537. MING Dongping, WANG Qun, YANG Jianyu. Spatial Scale of Remote Sensing Image and Selection of Optimal Spatial Resolution[J]. Journal of Remote Sensing, 2008, 12(4): 529-537.
[19] WOODCOCK C E, STRAHLER A H. The Factor of Scale in Remote Sensing[J]. Remote Sensing of Environment, 1987, 21(3): 311-332.
[20] LEES B.The Spatial Analysis of Spectral Data: Extracting the Neglected Data[J]. Applied GIS, 2006, 2(2): 14.1-14.13.
[21] DRĂGUŢ L, TIEDE D, LEVICK S R. ESP: A Tool to Estimate Scale Parameter for Multiresolution Image Segmentation of Remotely Sensed Data[J]. International Journal of Geographical Information Science, 2010, 24(6): 859-871.
[22] MING Dongping, LI J, WANG Junyi, et al. Scale Parameter Selection by Spatial Statistics for GeOBIA: Using Mean-shift Based Multi-scale Segmentation as an Example[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2015, 106: 28-41.
[23] MING Dongping, CI Tianyu, CAI Hongyue, et al. Semivariogram-based Spatial Bandwidth Selection for Remote Sensing Image Segmentation with Mean-Shift Algorithm[J]. IEEE Geoscience and Remote Sensing Letters, 2012, 9(5): 813-817.
[24] LI Peijun, GUO Jiancong, SONG Benqin, et al. A Multilevel Hierarchical Image Segmentation Method for Urban Impervious Surface Mapping Using very High Resolution Imagery[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2011, 4(1): 103-116.
[25] 许妙忠, 丛铭, 万丽娟, 等. 视觉感受与Markov随机场相结合的高分辨率遥感影像分割法[J]. 测绘学报, 2015, 44(2): 198-205, 213. DOI: 10.11947/j.AGCS.2015.20130453. XU Miaozhong, CONG Ming, WAN Lijuan, et al. A Methodology of Image Segmentation for High Resolution Remote Sensing Image Based on Visual System and Markov Random Field[J]. Acta Geodaetica et Cartographica Sinica, 2015, 44(2): 198-205, 213. DOI: 10.11947/j.AGCS.2015.20130453.
[26] POLAK M, ZHANG Hong, PI Minghong. An Evaluation Metric for Image Segmentation of Multiple Objects[J]. Image and Vision Computing, 2009, 27(8): 1223-1227.