A Methodology of Image Segmentation for High Resolution Remote Sensing Image Based on Visual System and Markov Random Field

doi:10.11947/j.AGCS.2015.20130453

Abstract

Abstract: In consideration of the visual system's tremendous ability to perceive and identify the information, a new image segmentation method is presented which simulates the mechanism of visual system for the high resolution remote sensing image segmentation with Markov random field model. Firstly, the characteristics of the visual system have been summarized as: hierarchy, learning ability, feature detection capability and sparse coding property. Secondly, the working mechanism of visual system is simulated by wavelet transform, unsupervised clustering algorithm, feature analysis and Laplace distribution. Then, the segmentation is achieved by the visual mechanism and the Markov random field. Different satellites remote sensing images are adopted as the experimental data, and the segmentation results demonstrate the proposed method have good performance in high resolution remote sensing images.

Key words: visual system, remote sensing image, wavelet transform, Markov random field, image segmentation

CLC Number:

P236

XU Miaozhong, CONG Ming, WAN Lijuan, XIE Tianpeng, ZHU Xiaoling. 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.

References

[1] XIAO Pengfeng, FENG Xuezhi, ZHAO Shuhe, et al. Segmentation of High-resolution Remotely Sensed Imagery Based on Phase Congruency[J]. Acta Geodaetica et Cartographica Sinica, 2007, 36(2): 146-151.(肖鹏峰, 冯学智, 赵书河,等. 基于相位一致的高分辨率遥感图像分割方法[J]. 测绘学报, 2007, 36(2): 146-151.)
[2] ZHAO Yuqian, LI Huifen, WANG Xiaofang. Multi-threshold Image Segmentation Based on Simulated Annealing Algorithm[J]. Application Research of Computers, 2010, 27(1): 380-382.(赵于前,李慧芬,王小芳. 基于模拟退火算法的多阈值图像分割[J]. 计算机应用研究, 2010, 27(1): 380-382.)
[3] LI Guoping, LU Changhou, LI Jianmei. Characters Edge Detection and Segmentation Method Based on Canny Operator[J]. Machine Tool & Hydraulics, 2007, 35(12): 42-44.(李国平, 路长厚, 李健美. 基于Canny算子字符边缘检测与分割方法研究[J]. 机床与液压, 2007, 35(12): 42-44.)
[4] HU Xuekun, SONG Shuna, LI Jinxia, et al. Image Segmentation Method Based on Hierarchical Clustering Least Squares Support Vector Machine[J]. Computer & Digital Engineering, 2010, 38(1): 143-146.(胡学坤, 宋淑娜, 李金霞, 等. 基于层次聚类LS-SVM的图像分割方法[J]. 计算机与数字工程, 2010, 38(1): 143-146.)
[5] CHEN Wufan. Wavelet Analysis and Its Application in Image Processing[M]. Beijing: Science Press, 2002.(陈武凡. 小波分析及其在图像处理中的应用[M]. 北京: 科学出版社, 2002.)
[6] ZHANG Kun, WANG Shitong. Improved Order-invariant Watershed Algorithm for Image Segmentation[J]. Journal of Computer Applications, 2008, 28(4): 969-972.(张鲲, 王士同. 一种顺序无关的改进分水岭图像分割算法[J]. 计算机应用, 2008, 28(4): 969-972.)
[7] LI F, PENG J. Double Random Field Models for Remote Sensing Image Segmentation[J]. Pattern Recognition Letters, 2004, 25(1): 129-139.
[8] MAO J, JAIN A K. Texture Classification and Segmentation Using Multiresolution Simultaneous Autoregressive Models[J]. Pattern Recognition, 1992, 25(2): 173-188.
[9] LIU G, WANG L, XIE W, et al. New Texture Segmentation Approach Based on Multiresoluton MRFs with Variable Weighting Parameters in Wavelet Domain[C]//International Symposium on Multispectral Image Processing and Pattern Recognition.[S.l.]: International Society for Optics and Photonics, 2007.
[10] LI Yunfeng, CAO Yukun, ZHU Qingshen. Multiresolution Segmentation Approach for Tree Image Based on Hidden Markov Tree Model[J]. Application Research of Computers, 2007, 24(8): 233-235.(李云峰, 曹渝昆, 朱庆生. 基于小波域隐马模型的树木类图像分割算法[J]. 计算机应用研究, 2007, 24(8): 233-235.)
[11] LI Xuchao, ZHU Shanan, ZHU Shengli. Image Segmentation Based on Wavelet Domain Hierarchical Markov Model[J].Journal of Image and Graphics, 2007, 12(2): 308-314.(李旭超, 朱善安, 朱胜利. 基于小波域层次Markov模型的图像分割[J]. 中国图象图形学报, 2007, 12(2): 308-314.)
[12] KAN XU, WEN YANG, GANG LIU, et al. Unsupervised Satellite Image Classification Using Markov Field Topic Model[J]. IEEE Geoscience and Remote Sensing Letters, 2013, 10(3): 130-134.
[13] XU Ke. Outline of Neurobiology[M]. Beijing: Science Press, 2005.(徐科. 神经生物学纲要[M]. 北京: 科学出版社, 2005.)
[14] MUNDER S, GAVRILA D M. An Experimental Study on Pedestrian Classification[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006, 28(11):1863-1868.
[15] GHOSH K,SARKAR S,BHAUMIK K. Understanding Image Structure from a New Multi-scale Representation of Higher Order Derivative Filters[J]. Image Vision Computer, 2007, 25(8):1228-1238.
[16] LUO Siwei. Information Processing Theory of Visual Perception[M]. Beijing: Publishing House of Electronics Industry, 2006.(罗四维. 视觉感知系统信息处理理论[M]. 北京: 电子工业出版社, 2006.)
[17] FIELD D J. Relation between the Statistics of Natural Images and the Response Properties of Cortical Cells[J].Journal of the Optical Society of American, 1987, 4(12):2379-2394.
[18] LIU Guoying, MA Guorui, WANG Leiguang, et al. Image Modeling and Segmentation Based on Markov Random Field with Wavelet Transformation in Matlab[M]. Beijing: Science Press, 2010.(刘国英, 马国锐, 王雷光, 等. 基于Markov随机场的小波域图像建模及分割-Matlab环境[M]. 北京: 科学出版社, 2010.)
[19] YU Peng, ZHANG Zhenlong, HOU Zhiqun. Textured Image Segmentation Based on Gauss Markov Random Field Mixture Model[J]. Acta Geodaetica et Cartographica Sinica, 2006, 35(3):224-228.(余鹏, 张震龙, 侯至群. 基于高斯马尔可夫随机场混合模型的纹理图像分割[J]. 测绘学报, 2006, 35(3):224-228.)
[20] CHEN Z, WANG L G,CHEN R Y, et al. Image Segmentation Using Multiregion-Resolution MRF Model[J]. IEEE Geoscience and Remote Sensing Letters, 2013, 10(4):816-820.
[21] ZHAO Bei, ZHONG Yanfei, ZHANG Liangpei. High Spatial Resolution Remote Sensing Image Segmentation Based on Multi-agent Theory[J]. Acta Geodaetica et Cartographica Sinica, 2013, 42(1):108-115.(赵贝, 钟燕飞, 张良培.高空间分辨率遥感影像的多智能体分割方法研究[J]. 测绘学报, 2013, 42(1):108-115.)
[22] 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.(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.)