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国土资源遥感  2017, Vol. 29 Issue (2): 72-81    DOI: 10.6046/gtzyyg.2017.02.11
  技术方法 本期目录 | 过刊浏览 | 高级检索 |
基于纹理特征与区域生长的高分辨率遥感影像分割算法
苏腾飞, 张圣微, 李洪玉
内蒙古农业大学水利与土木建筑工程学院,呼和浩特 010018
Segmentation algorithm based on texture feature and region growing for high-resolution remote sensing image
SU Tengfei, ZHANG Shengwei, LI Hongyu
Water Conservancy and Civil Engineering Institute, Inner Mongolia Agricultural University, Hohhot 010018, China
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摘要 影像分割是面向对象影像分析中的重要步骤。为了提高高分辨率遥感影像(high-resolution remote sensing image,HRI)分割算法的性能,提出一种新的影像分割算法,包含种子确定、基于种子区域生长(seeded region growing,SRG)的过分割(advanced SRG,ASRG)和层次区域生长(hierarchical region growing,HRG)3个步骤。利用Gabor纹理特征定义纹理均匀性,将种子自动放置在HRI中同一纹理组成区域的中心位置; 在SRG阶段,将HRI光谱信息与斑块形状信息相结合,提出了一种新的合并规则,以提高SRG过分割的精度与分割结果中各个斑块排列的紧凑性; 在HRG阶段,提出了一种自适应的阈值,可以更好地保持多尺度分割的特性; 在实验部分,采用3景HRI验证了上述方法。利用监督的影像分割评价方法定量评价了该方法的分割精度,并与另外2种主流的遥感影像分割算法进行了对比。结果表明,该方法可以得到令人满意的分割效果。
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关键词 巴颜喀拉山群岩性解译岩性组合划分解译标志SPOT5    
Abstract:Image segmentation plays an important role in object-based image analysis. In order to enhance the performance of segmentation method for hierarchical region growing (HRG),this paper proposes a new image segmentation algorithm. The new method consists of three steps: seed determination, seeded region growing (SRG)based over-segmentation (advanced SRG, ASRG) and HRG. To improve the automation and precision of seeds determination, the authors used Gabor texture feature and defined textural homogeneity, attempting to place the seeds at the center of the regions composed of the same texture. At the stage of SRG, spectral information of HRI was combined with shape cues to form a new merging rule to raise the segmentation accuracy and segments compactness of SRG over-segmentation. At the HRG step, an adaptive threshold was used to better retain the multi-scale segmentation property. In the experiment, three scenes of HRI were utilized to validate the proposed method. A supervised segmentation evaluation method was adopted to quantitatively assess the segmentation accuracy of the proposed algorithm, and two state-of-the-art segmentation methods were compared with the proposed method. The experimental results show that the new algorithm proposed in this paper can produce satisfying segmentation.
Key wordsBayan Hara Mountain Group    lithological interpretation    partition of lithological association    interpretation key    SPOT5
收稿日期: 2015-05-05      出版日期: 2017-05-03
基金资助:国家自然科学基金项目“科尔沁沙地典型生态系统水热通量传输机理及其与植被耦合关系试验和模拟研究”(编号: 51569017)、“内蒙古典型草原水文过程及其扰动与触发草地退化的水文临界条件实验与模拟研究”(编号: 51269014)和中国博士后科学基金面上资助“西部地区博士后人才资助计划”(编号: 2015M572630XB)共同资助
通讯作者: 张圣微(1979-),男,博士,教授,硕士研究生导师,主要从事定量遥感、生态水文及气候变化等方面的研究。Email: zsw_imau@163.com。   
作者简介: 苏腾飞(1987-),男,硕士,实验师,主要从事面向对象的遥感图像分析算法方面的研究。Email: stf1987@126.com。
引用本文:   
苏腾飞, 张圣微, 李洪玉. 基于纹理特征与区域生长的高分辨率遥感影像分割算法[J]. 国土资源遥感, 2017, 29(2): 72-81.
SU Tengfei, ZHANG Shengwei, LI Hongyu. Segmentation algorithm based on texture feature and region growing for high-resolution remote sensing image. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(2): 72-81.
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http://www.gtzyyg.com/CN/10.6046/gtzyyg.2017.02.11      或      http://www.gtzyyg.com/CN/Y2017/V29/I2/72
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