| 视觉感受与Markov随机场相结合的高分辨率遥感影像分割法 |
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| 引用本文： | 许妙忠,丛铭,万丽娟,解天鹏,朱晓玲.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. |
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| 作者姓名： | 许妙忠 丛铭 万丽娟 解天鹏 朱晓玲 |
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| 作者单位： | 武汉大学 测绘遥感信息工程国家重点实验室, 湖北 武汉 430079 |
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| 基金项目： | 国家973计划(012CB719900)Foundation support The National Basic Research Program of China(973 Program) |
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| 摘 要： | 鉴于视觉感受对外界强大的感知与识别能力,模拟视觉神经感知的工作机制,并结合Markov随机场模型,提出一种影像分割方法。首先,分析视觉感知系统的工作机制,将其特性归纳为等级层次性、学习能力、特征检测能力和稀疏编码特性,继而利用小波变换、非监督聚类、特征分析和Laplace分布模拟视觉工作机制,然后结合Markov随机场模型实现高分辨率遥感影像的分割。通过不同卫星的真实遥感影像进行了相关试验。试验结果表明本文提出的方法在高分辨率遥感影像分割任务中有非常良好的表现。
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| 关 键 词： | 视觉感知系统 遥感影像 小波变换 Markov随机场 影像分割 |
| 收稿时间： | 2013-12-03 |
| 修稿时间： | 2014-06-18 |
A Methodology of Image Segmentation for High Resolution Remote Sensing Image Based on Visual System and Markov Random Field |
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| XU Miaozhong
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CONG Ming
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WAN Lijuan
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XIE Tianpeng
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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. |
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| Authors: | XU Miaozhong CONG Ming WAN Lijuan XIE Tianpeng ZHU Xiaoling |
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| Institution: | State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China |
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| Abstract: | In consideration of the visual system’s tremendous ability to perceive and identify the informa‐tion ,a new image segmentation method is presented which simulates the mechanism of visual system for the high resolution remote sensing image segmentation withMarkov randomfield model .Firstly ,the charac‐teristics of the visual system have been summarized as :hierarchy ,learning ability ,feature detection capability and sparse coding property .Secondly ,the working mechanism of visual systemis simulated by wavelet transform ,unsupervised clustering algorithm ,feature analysis and Laplace distribution .Then ,the segmentation is achieved by the visual mechanism and the Markov randomfield .Different satellites remote sensing images are adopted as the experimental data ,and the segmentation results demonstrate that the proposed method has good performance in high resolution remote sensing images . |
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| Keywords: | visual system remote sensing image wavelet transform Markov random field image seg-mentation |
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